{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>085</td>\n",
       "      <td>950700</td>\n",
       "      <td>27085950700</td>\n",
       "      <td>9507</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>25065045</td>\n",
       "      <td>38157</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-94.20405 44.79030, -94.20392 44.790...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>017</td>\n",
       "      <td>070200</td>\n",
       "      <td>27017070200</td>\n",
       "      <td>702</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>10082553</td>\n",
       "      <td>40680</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>POLYGON ((-92.46309 46.71567, -92.46294 46.715...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>017</td>\n",
       "      <td>070600</td>\n",
       "      <td>27017070600</td>\n",
       "      <td>706</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>985105496</td>\n",
       "      <td>10301452</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-93.06487 46.67780, -93.06483 46.678...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>017</td>\n",
       "      <td>070100</td>\n",
       "      <td>27017070100</td>\n",
       "      <td>701</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>22518036</td>\n",
       "      <td>1575104</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>POLYGON ((-92.49295 46.76249, -92.49278 46.765...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>017</td>\n",
       "      <td>070300</td>\n",
       "      <td>27017070300</td>\n",
       "      <td>703</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>104451183</td>\n",
       "      <td>1748285</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON ((-92.42850 46.75665, -92.42848 46.757...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        27        085    950700  27085950700   9507  Census Tract   G5020   \n",
       "1        27        017    070200  27017070200    702  Census Tract   G5020   \n",
       "2        27        017    070600  27017070600    706  Census Tract   G5020   \n",
       "3        27        017    070100  27017070100    701  Census Tract   G5020   \n",
       "4        27        017    070300  27017070300    703  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   25065045     38157  ...       88        6        0       82   \n",
       "1          S   10082553     40680  ...       70        0        0       70   \n",
       "2          S  985105496  10301452  ...       51        0        0       51   \n",
       "3          S   22518036   1575104  ...       99        0        2       97   \n",
       "4          S  104451183   1748285  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        6        0        0        6   \n",
       "1        0      180       75        0      105   \n",
       "2        0        0        0        0        0   \n",
       "3        0       29        0        0       29   \n",
       "4        0       10        0        0       10   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-94.20405 44.79030, -94.20392 44.790...  \n",
       "1  POLYGON ((-92.46309 46.71567, -92.46294 46.715...  \n",
       "2  POLYGON ((-93.06487 46.67780, -93.06483 46.678...  \n",
       "3  POLYGON ((-92.49295 46.76249, -92.49278 46.765...  \n",
       "4  POLYGON ((-92.42850 46.75665, -92.42848 46.757...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/mn_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e4af1be-0f6a-4c46-9d23-da70d63c30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1505 popn tracts for MN\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"MN\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MN voter-age population\n",
      "state pop= 5706494 VAP pct Hispanic is  0.04984400891950459\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MN\n",
      "total state pop= 5706494 , VAP pct Black is  0.0593374440337997\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LTl3PIGDnjj1sf3EnRw7v565dQlYB9VENUA2GLPdqfPjvP/zG2LG9D3U5EgBLC6/wxdxPud7dAMB3dr2ZT/74/lO30Ziz0JiGbrTUExwbVA2XX6qqjwza7CngPWOt6Fhdcu1Klr1pBc+t38BvHv0NDzrPMcev43UvLCa6tBaJOMQvqD8hQKdC4UCGI3duKV21A2ixHJICkQVV1NyylNi5taP6KySVSvGBD3yA73//+9x999187GMfIxo9xbDLpR+CQ5uofvo7/GN4Cw/7l9PT1cTtv32Z5qoYzVUxLppdhd9zlEMv72Dzlp08vK2bjV41bZHG8k6aOHYV6lyvG8Qt19VFxAFxBt4XikNVJMzH33QOoh5hP8tX2/bixvIkQt0k/Swpv3/gva7QT1izJP0sc/Jt3ND3OP2RGOvqbmHj44e44i03nPkHb8xZalRfxoqIC2wAlgDfOvnMXUT+E/iZqv50iLK7gU5K52jfVdXbhznGrcCtAPPnz3/d3r17T7MpJyoWizz77LP89te/IefnmePXscJbSIvWEmosXb+tvlJ9/QISlzaN6Vinq+POVnJbj56wLNSUoOot84mdW4sTO/2vTnbs2MHdd9/N0qVLef/734/jnKIz84s8d/ufsfjQQ9RIH0V1eSY4n0eDS/l1cClHvTQrup7jleRCemPVJCTHAu8oK+J5FjYlqE64JJ0CKxbGSDg5KGSg0Af5DBR6B033nTjv50fVlgChz42TcRP0nXcTs6/8DD/6H/+D6uZZfOAr38CxK3KMeY1xu+pGRGqA+4BPq+rm8rLbgJXAu3WInYnIbFU9ICJNwNpy2d+e6jjjeR19PpvnmSee5KnnnqEv209LrIHL65cxPz6L3OYjpK6aQ82Ni8flWKOlxYC+5w7Tdd9OQg2lTsfryJ6wTfzCesJzUkjYRQs+oYYYoaYkbqp8ff4Ql3Q+/fTT/PKXv+T1r389b3vb24Y8dn9PN4d37eTQy9tpe3k77HuGWbqHxelOGqN9Q5YZkROGaAoix17JE+ej5WWR9KB1J88f33bdPXtpfeLQwO7rZz3Bwe3P8ZGvf5OGeQvOrI7GVLhxu+pGVbtEZB2wBtgsIh8FbgKuGyrky2UOlN/bROQ+YBWlL3cnRTQe5arrr2b11Vfy/PPP88QTT3D//t/S0tLChU4jF/Q2ol4wqcM5EnZIXdFC6oqWgWX53d3k9/bQ+9g+NO+TbT1CtvXIMDsAJx5CIi7qB7hVURKXNHL561dy5MgRnnzySRoaGrjogvPLob6Dwy/v4NCunfS0Hx7YTW3LHFJNb2CX9w6ea0+TwOe6+v+Xue529i66lXmLWnBi6ZPCe4hwDkXG9fN58wcvGAh6v7CD/Vuf4co//LCFvDFnaDRfxjYCxXLIx4FHgK8DHvD3wJtVtX2YsknAUdXe8vRa4G9U9aFTHXMiH4HgeR6bNm3i8ccfp7Ozk+ogwcX+fC5oWUpiThWxc+uIX1iPFn2CrPea8k4qcvxa/AmmXoAWfPxMkSBTxO8rlN4zRYK+IsUDGQqv9J5Q5tAVR3l8yya6Cx7xV7YT6i+tr25qpvmcc2lasBhxmjnalmRfa4ZCzieaCLF4RSPnvK6JuefX4rpT+x3GYz99iS1PHECDLPmeH9O4oJkPffXvccfj+T7GVKgxDd2IyCXAnYBL6bHG96jq34jITkqXXB477XxKVT8pIrOBO1T17SKymNJQD5T+erhbVb86UoUnMuiP8X2fFx5bz1ObnqWtp4M4EZb5c7moZglz3ncxbf+8cchyydUt1L7z1FeoTDSvWKRj724OvbyDQ7t20P9yB6tCx4dqclrgwehz9Ememy55A4uuuZSOfR47n2tjzwsd0zLcAbyCz7q7trHt6dLZfKHvIdR7iQ/97T/QvOicKa6dMdPbWXVn7OlSVXbv3s2TTz7Jjh07EIVmrWG+38CS+vnULZ9Fvq+PaDJJbu1houdUU/uec3HKz6hRL8DvKbxmv06stH6svEKRV57aRnvrbvo6DnO0fR99PW2IKoJDOJqgqq6FdG0LCzvnIgj5sM8h/yhrI5u40JtHV2bJtA33Qs7j6IE+nnt4L/u2HsUrlK5AWn2zy7offYMr3vU+3vj+j0xxLY2Z/izoR+ngrv28uH4jOw/spq2rdNm/k8vi9vcgXpHLa64jSYyYhokTYdbNy+j7zz3gv/YzlLBDy1+vPuGa99Pl5Qu8fNtakqETHxZcwKNXsmQkN+g9R0ay9EqOghwfcrqiuJToeZdNq3A/JtdX5If//QmC4PjnN2txFdf+0Tnc8zf/lWg8wYe/9k1C4bF3mMZUOgv6M9DZ2cm2bdt48rFH6c5mh33IVkgdYkRI1VSRiMZJRGOEDnmE+iG2pAac0iONhWPPnj9WctA4v5wwx7ENM729bDu4i6gvxBtqyPoFerO9FLwT/4IIuSGqEmnSiarSe7KKqmQV6XiKc688n3B4eo5tB4Hy+5/v5IVf7RtYpkGWYv8jBN4uPviV/0PL0vOmsIbGzBxnzbNuxlNtbS2rV69m9erVABQKBfr6+ujv7yeTydB7sJO+TB99uX5yFOjL9tPf309Hdyd9fgY/HMDYbgUoEciEoDGcoq6xjsU1i6mpqTnhlUgkZswjHgZzHGH1zYvZuPYV1D+Il99EUNgGBJx35S0W8saMEwv6UYpEIkQiEWpryw9BO0UGqSpevli6s3/QowEGD1Ec37j8IyXHZga97Vz/Io/86B+4/MY/5U3vv37sjRgHvh/gFwKKBR+/GOAVAryij+8pgRfgewG+X5oOfCVZE6V+TpKejhyP/mQr7a90M2ux4LoZirmjHNi+j6C4Cw2OAGHOWXk1l665kQUXnzvVTTWmYljQTwARIRwb+7Xluf4sFPqorp+aJ2yqKg/80wu8sqV0F6/jyGs6q9IvaeVRzYJm0SCHan95urysPK1BL2iGvc8N3oODuM2suOGPueKdbyNVk568BhpzlrCgn8YynaWnVqYbayZk/xooB1/uom1vL4Gv9B7Jkc1kKeYy9HR00tN+lGI+UwppzdIwN0RQ7KdYyODl+yjm+ijkMmjgD7l/xw0RTaRxwwmQJA1zF9OydD7VTbOobmymqqmZVF2dPdLAmAlmQT+N7dtS+q3VJ+/9Ba3ranHcMG4oghsO4YQiOKEwoVAENxQmUROndlYa1w0RjoUJRUIEXpF8tkDb3k52rt9LKFIk291Ntrcb3+unv6cHgn5UcwNn5Ghx6MqI0HM4TTxdRbK6inh6AfGqKuLp0itRVT0wHa+qIl5VTTgam5HfHRhTaSzopzE3UhrGOLT9EQ6NsO3pctwo4WiSUDRFVeMsqhpqSdXWnBDUA8GdriKWStmZtzEzlAX9NPahv/kw+dx78fMFioUCXj5PsVDALxQoFvJ4hSJesUDf0X56jvQS+D5BEOAVigS+z77NvyFV18KCi1cx65xZVDfVlUI8VUUoMr7PpzHGTF8W9NNcNBaF2Jn+rN+Hx7UuxpiZafrcJmmMMWZCWNAbY0yFs6A3xpgKZ0FvjDEVzoLeGGMqnAW9McZUOAt6Y4ypcBb0xhhT4SzojTGmwlnQG2NMhbOgN8aYCmdBb4wxFc6C3hhjKtyIQS8iMRF5RkReEJFWEflyefk3ROQlEdkkIveJSM0w5deIyDYR2SkiXxjn+htjjBnBaM7o88C1qrocWAGsEZHVwFrgIlW9BNgOfPHkgiLiAt8CbgCWAR8QkWXjVHdjjDGjMGLQa0mmPBsuv1RVH1FVr7z8KWDuEMVXATtVdZeqFoB/AW4eh3obY4wZpVGN0YuIKyIbgTZgrao+fdImHwN+OUTROcC+QfOvlpcZY4yZJKMKelX1VXUFpbP2VSJy0bF1InIb4AF3DVF0qF+G1qGOISK3ish6EVnf3t4+mmoZY4wZhdO66kZVu4B1wBoAEfkocBPwIVUdKsBfBeYNmp8LHBhm37er6kpVXdnY2Hg61TLGGHMKo7nqpvHYFTUiEgfeArwkImuAzwPvUNX+YYo/CywVkUUiEgHeD9w/LjU3xhgzKqP5cfAW4M7yFTQOcI+qPiAiO4EosFZEAJ5S1U+KyGzgDlV9u6p6IvIp4GHABX6gqq0T0xRjjDFDGTHoVXUTcOkQy5cMs/0B4O2D5n8B/GIMdTTGGDMGdmesMcZUOAt6Y4ypcBb0xhhT4SzojTGmwo3mqhszyfLFAk+s38CrezvoPpijWPRI18RZ0DKHlqYGmprqSNfFSKQjiDPUPWnGGHOcBf00kusrsuWJA/zmly8SySWAJIVQQBByye6Os/O5XnbSC+wGQEUhWcRJB4RSSrgaqhsTtLTUsXDeHJqb63GsIzDmrGdBPw10He5n06P72PrkQbxCQFv1PjbPf4LD6d08+fHHcR2XXV272HpgO+1tXTy/+0V6j+YI5xLEsimSfTWkjtaQKtTSr8pBjvAcR/DFIx/PEKTz1DWnuHDxUlpa6qlqjFPdECcUcae66caYSSBDP7lgaq1cuVLXr18/1dWYcJu37eDx+14i2JMAB5xzM7jLu/mnV74xsM1TH3yKZDg57D5UlUJQIOfl6C/0s+/QIfYfaKPjUA89HTkKnQo9EaJ9aaJ+/ISy2dqj/Nl/v4l0XWzC2miMmRwiskFVVw61zs7op8je1iM89v/tIY/SOudhWmc9QTbSC6+cuN2Ozh2saFox7H5EhKgbJepGqY5W05JugaWv3W770e2s3fYofUeKSE+E1l07WX7wau79+npu+vRyGuamx7eBxphpw87op8CW3x1g3V3byFZ18cyl9/Lj93x/yO0EIeJGxv34qsr7HngfuTZ4947PUMh53PDJi5l3ft24H8sYMzlOdUZvl1dOIlXlmQd289hPXqKrfj//seybJGsiA2fkJ78mIuQBdnTtYOvRrVx47jnc8levI10X44F/eoHtzxyakOMZY6aWBf0k8f2Ax37yEs8+sJvCknbuXfKPXNByHu9e+u5Jr8st998CwCWNl5Cui/Huz13GrMXVrP3BFp57ZC/T8a88Y8yZs6CfBIWcxy++vYmtvz/IyhsXsu2SxzinbjHfvf673LT4pkmvz+cv/zwAf/f03/HXv/treqWbd/zlCpa8roknf/4yj9+zgyCwsDemUtiXsROsryvHg99+kY5Xe6m+vp/9529k+/PbWdk85FDapPjwsg/zB+f8Ad/b9D3u3HIn/77z37l63tWwGJp6L+LFx2DzwVb+4rPvmrI6GmPGjwX9BAryBX72l/eRTTRzyaZvs/3gFr7+ztK1673F3impkx/4dBe6KfgFPrLsI9TEavjZtp+xo3MHrrjsXbyXizpuoGHnvJF3ZoyZESzoJ5BX8ChEawDYueQWQrnFXCJH2BQ8w5qFawDI+3l6C73UxepwZOwjacWgyEd+8RFaj7TSFG8i7IYH1uX9PJ25Tnz1CftR0rk60vl6GvLnks7Xkc7XUZWrp6F/Lv2xrjHXxRgzPVjQT6BIOsGHv/J6Xt5wiN2tSQ7saeYNv4dLIjeyqbWX9c4dZJ0+im4eDXkkkjHSySSpZIJEPIaEIRoJEwq7JGMJ0vEk+U4llBBCg+6hKvpFCn6BglfAcVza9vbSpPOhV2gMzeP80MW4fXEifQki/SmcTBTNndSphAKkykMaPTTdxUUXNUzuh2WMmTB2Hf0kymYKvPjsHh5/egNacAn7UcJeBCm6+AVFCw5OMDGPJXBDDun6GFUNMdL1carqY6X5+jjp+hjxdJjyT0IaY2YguzN2moinIqy65lxWXXPusNv4XkA+W6Q704vvBfTnsuQLRTLZPnr7+ziyuUis3iHe6AwEc8gJEXbDhN0wgfp4gUchKOAFHktnLaalpcGedGnMWcyCfppxQw6JdJREOjr0Bm+a3PoYY2Y+u47eGGMqnAW9McZUOAt6Y4ypcBb0xhhT4UYMehGJicgzIvKCiLSKyJfLy99bng9EZNj7+UVkj4i8KCIbRaTyrpk0xphpbjRX3eSBa1U1IyJh4AkR+SWwGXg38N1R7OMaVe0YQz2NMcacoRGDXkt3VGXKs+HyS1V1K2A32RhjzDQ3qjF6EXFFZCPQBqxV1adP4xgKPCIiG0Tk1lMc41YRWS8i69vb209j98YYY05lVEGvqr6qrgDmAqtE5KLTOMaVqnoZcAPwX0RkyFt+VPV2VV2pqisbGxtPY/fGGGNO5bSuulHVLmAdsOY0yhwov7cB9wGrTueYxhhjxmY0V900ikhNeToOvAV4aTQ7F5GkiKSPTQNvpfQlrjHGmEkymjP6FuAxEdkEPEtpjP4BEXmXiLwKvB54UEQeBhCR2SLyi3LZZkpX6bwAPAM8qKoPjX8zjDHGDMceU2yMMRXgVI8ptjtjjTGmwlnQG2NMhbOgN8aYCmdBb4wxFc6C3hhjKpwFvTHGVDgLemOMqXAW9MYYU+Es6I0xpsJZ0BtjTIWzoDfGmApnQW+MMRXOgt4YYyqcBb0xxlQ4C3pjjKlwFvTGGFPhLOiNMabCWdAbY0yFs6A3xpgKZ0FvjDEVzoLeGGMqnAW9McZUOAt6Y4ypcBb0xhhT4UYMehGJicgzIvKCiLSKyJfLy99bng9EZOUpyq8RkW0islNEvjCelTfGGDOy0Ci2yQPXqmpGRMLAEyLyS2Az8G7gu8MVFBEX+BZwPfAq8KyI3K+qW8ZedWOMMaMxYtCrqgKZ8my4/FJV3QogIqcqvgrYqaq7ytv+C3AzYEFvjDGTZFRj9CLiishGoA1Yq6pPj3L/c4B9g+ZfLS8b6hi3ish6EVnf3t4+yt0bY4wZyaiCXlV9VV0BzAVWichFo9z/UKf7OswxblfVlaq6srGxcZS7N8YYM5LTuupGVbuAdcCaURZ5FZg3aH4ucOB0jmmMMWZsRnPVTaOI1JSn48BbgJdGuf9ngaUiskhEIsD7gfvPsK7GGGPOwGjO6FuAx0RkE6XgXquqD4jIu0TkVeD1wIMi8jCAiMwWkV8AqKoHfAp4GNgK3KOqrRPREGOMMUOT0kU108vKlSt1/fr1U10NY4yZMURkg6oOeU+T3RlrjDEVzoLeGGMqnAW9McZUuNE8AsEYM009d7Cb9/3gabxCAIBI6W51xxXckIPrCuGwSzzqko6FScdC1MTD1CciNCQiNKcizElGaUnGqEuGqYqFSUTcke54NzOMBb0xM9h/bm+j2FuktjlBVVWUQMELAjxfKXgBfjEgV/DozRQ4WMigXoAEI+xUQEIObsTBjbgkYiGqUxEa01GunVPLBQ0p5tTEmV0TJxm1CJkJ7F/JmBlsX08WgHs/dgWLqxMjbp/zAw5l8+zPFDjQl+NgJk97f5GO/gJd/UWyBY9s3iOX98nnfXIFj/5+j67OHLtzPs8+d+iE/VXHw8yuiTO7OlZ6r4kzuybGnJo4LTVxmtNRQq6NEE81C3pjZjA/76MCeXd028dch4WpOAtTcaD6tI713ud28Pihbhbh8t9mNdLek+dAV5YDXVn2d2VZv7eT7mzxhDKOwKyq451AS7kTmF19vFOojodtqGiCWdAbM4NFfSDssK0vzwWpkc/ox+KHlyzm+sI2dmcL/F2umw1XX/iabTJ5j4Pl4D/YnRvoBA50Zdm4r4uHNuco+CeOHSUi7kBHMKcmRkv1iX8ZzKqOEQ2NsiczQ7KgN2YGy2Y9NOKQ8UcaeB+7pOtwcTrB7myB/fki+SAg6pw4LJOKhljanGZpc3rIfQSB0tGX50BXbuCvgYHp7ixbDvTQkcm/plxDKsqcmkF/GVTHBr4nmF0Tpz4ZwXHsr4LhWNAbM4P1Zoto2KHf9yf8WArc39Y1MP/Zra9wXjKGK0K/H9Dp+XQVPbqKPumQy/nJGK+vSXFhKkZVqHQlj+MITekYTekYF8+txuG1v2mRK/oc6j4W/rkThoe2H+7lsW1t5IondmziCJFEiFgyzOzaOG9ZWM/qubUsbUrRmI6e9UNDFvTGzGA7uvoh5tA3CWf0jgh3XryIH+3vICzCsz193FcOfgeoCbvUhEJUhVxeyRV4oL1r4JnkKdehPhwiJELG9+nxArJBQFiEdMgh6bpUh1zCIoQdwRUQBE8UpxaOpiIU5oRoL8Tp83woKrGCz3xc/H6fYn+RQr9Htq/AlpePsvWlI/xT+djxqMv5zWnOa06zpCnFuc1pljanmFUVO2s6AAt6Y2aw3mwRTce4ofH0vlg9U29rqOZtDceP1e8HOEDUkdeEZnfR45nuPnb259mfL9BZ9CmqknIdqkIuKdclFwT0eD79fkC35+OpUgwUT5UAJSKCp8rSZJSQCE2REEsTMS6rSnB+Mk5oiOGag7kCP9zVxu/2HWVbW4bengKt/Tl2t/bR9ezxL4tT0RBLmlIsbUqxtDnF0qZSRzCnJl5xw0AW9MbMUL4foMWAlQ1pzk/Gp6QOiVNcOlkdDnF9QzXXT2J9AFpiEf6fZXNh2Vx6PZ+lj79ID9ADRIoBxd4iH6+uxu3z2NGWYd32dv51w6sD5eNht9QBlMP/WEcwtzaBO0M7AAt6Y2aoQ/0FRKE2GZnqqkxb6ZDL06sv4NVcgd3ZAnfu7+DFcJY7yHHh7BjLzm3hqmScRKAcPpqluzPH9sMZjnZmeWJnBz9/bv/AvqIhh3Maj3UAKZY0pTm3OcX8usS0v1fAgt6YGWpfbw6Aegv6U1oQj7IgHuXKWvhASx137u9gT7bAjv4cjx7p5V8PdR7f2AFaQtCSBtJcm06yKHA5dLSf9qNZjhzNsnZnO/+x8fgP5UVch8WNyfIwUHqgI1hQnyQSmh4dgAW9MTPUi0f7APh162H+W6A4jhB2HEKO4DqlLzVDjkPIFUKOEHEcQi4D20Sc0rNwIo4Qdp3yeiHqHlt/rIxD1C3tO+I6RMplXHntuPx054rwsbkn/ib1/lyBoiqq0Ov7dBQ8PvPSK7QXPB7tLX3GRCE0O4zXEgaqwAuIZ32aChDr98n0FnlqbycPbjo48AV0yBEWNSRZ2lw6+z82BLSoITnp9wVY0BszQ+3I51Ggc3+Ge/dnJv34KoAAIkj5nfIyGbRMRBDn+DJx5PjD1watcxzBKa93BuYFxykFtOOUXsem3ZOnHcF1wB3U2bkihNzS+7HOz3WFkBzvAEODOshQudP7TLqGsCsIpTYlQy5RV/AFvrLrIPFQmJbGJK/mC+wtFMk1h0GSECjS7+H0e/xpXR17O/rYerCXhzYfIij3AK7AnBpYUFtgcb3LebOqWb7wEs5pqiIWnpgOwH5hypgZKucHvNiZAU9xFPJBgBcoBT+gGChFXykGpWlv0DJfAwq+4gVaegBaefmxeV+h6Af4QWmZX34Nnva1tG0QMDDvB0pQXh/o8fcgKN0opVpaf2zZ8fnStJ40rQqUz7RLy47PlxZSfpWnp6mQI3jB6Cu452s3ntFxTvULU3ZGb8wMFXMdLm+omupqTBtBuaMq+EreDyiUO7mCH1DwA7wA8n4w0PkV/eMdXtEPKJTLlzrI8nS54yr6QbkjPLGD9AZ1gl6g9Hk+rT1Zcn7AdXVplsSjA9vs3vs9AnVxQ7V4AThuFfmi8kpXhH29s/A1RMSdmB7Lgt4YUxEcR4g4LpEQpKa6MkP6+yk78vT4StgYY8yEsaA3xpgKZ0FvjDEVzoLeGGMq3IhfxopIDPgtEC1v/2+q+iURqQN+BiwE9gB/qKqdQ5TfA/QCPuANd/mPMcaYiTGaM/o8cK2qLgdWAGtEZDXwBeDXqroU+HV5fjjXqOoKC3ljjJl8Iwa9lhy77S5cfilwM3BnefmdwDsnooLGGGPGZlRj9CLiishGoA1Yq6pPA82qehCg/N40THEFHhGRDSJy6ymOcauIrBeR9e3t7afVCGOMMcM7rUcgiEgNcB/waeAJVa0ZtK5TVWuHKDNbVQ+ISBOwFvi0qv52hOO0A3tHXbHx0QB0TPIxJ5q1aeaoxHZZmybXAlVtHGrFad0Zq6pdIrIOWAMcFpEWVT0oIi2UzvaHKnOg/N4mIvcBqyh9uXuq4wxZ2YkkIusr7TsEa9PMUYntsjZNHyMO3YhIY/lMHhGJA28BXgLuBz5a3uyjwH8MUTYpIulj08Bbgc3jUnNjjDGjMpoz+hbgThFxKXUM96jqAyLyJHCPiHwceAV4L5SGaoA7VPXtQDNwX/mZ1SHgblV9aALaYYwxZhgjBr2qbgIuHWL5EeC6IZYfAN5ent4FLB97NSfF7VNdgQlgbZo5KrFd1qZpYlo+j94YY8z4sUcgGGNMhbOgN8aYCndWBb2IrBCRp0RkY/nmrFUnrZ8vIhkR+dwI+/mciKiINExsjUc21jaJyDdE5CUR2SQi9x27wmoqjUOb6kRkrYjsKL+/5v6OyTZcm0RkVXnZRhF5QUTedTrlp9JY21Te9tMisk1EWkXkf09e7Yc3Hu0qbz9tcqL0W4xnyQt4BLihPP12YN1J6+8F/hX43Cn2MQ94mNINXQ0zvU2ULnkNlae/Dny9Atr0v4EvlKe/MJ3bBCQGff7H7kcJne5nMkPbdA3wKyBanm+a6jaNR7vK66dVTpxVZ/SUHsdw7Ec2q4EDx1aIyDuBXUDrCPv4B+CvmD4/RzymNqnqI6rqlWefAuZOTDVPy1j/nabjc5iGbJOq9g/6/GMM///VsJ/JFBprm/4c+Jqq5svlhrzpcgqMtV0w3XJiqnuaSe6pL6B0zf8+YD+lW4YBksCTlH5q8n8x/JniO4Bvlqf3MA166rG26aR9/Sfw4ZneJqDrpPnO6dqm8rorKHVcGeBdp1t+BrdpI/Bl4GngN8DlU92mcWrXtMuJivtxcBH5FTBriFW3Ubru/7+q6r0i8ofA9ynd6ftl4B9UNVO+uWuo/SbK+3jrhFT8FCaqTScd4zbAA+4at4qf+ngT3qbJdoZtQksPCbxQRC6gdHPiL1U1d9I+/ny48hNpgtsUAmqB1cDllG7AXKzlhJxIE9WuqcyJU5rqnmaSe+pujt87IEBPefpxSj3vHqALOAp86qSyF1Makzu2nUep1581U9s0aB8fpXSmnJjqf6PxaBOwDWgpT7cA26Zrm4bY7jFg5ZmWn2Ftegi4etD8y0DjTG7XdM2Js22M/gDw5vL0tcAOAFW9SlUXqupC4B+Bv1XVfx5cUFVfVNWmQdu9Clymqocmq/LDOOM2AYjIGuDzwDtUtX9SajyyMbWJUTyHaQoM2SYRWSQiofL0AuA8SgExqvJTbKxt+vdyOUTkXCDC9Hgy5Bm3a7rmRMUN3YzgE8A3y/9YOWDY5+MfIyJ3AN9R1fUTXbkzNNY2/TOln4lcWx4OeUpVPzmB9R2NsbbpawzxHKYpNlyb3gh8QUSKQAD8hap2wGvadNqfySQYa5t+APxARDYDBeCjWj4tnmJjbde0Y49AMMaYCne2Dd0YY8xZx4LeGGMqnAW9McZUOAt6Y4ypcBb0xhhT4SzojTGmwlnQG2NMhfv/AahBgdHVxcVMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for GA)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "\n",
    "lakeTracts = [0]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y+0.00,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 9999:  #for Tennessee, these ALL appear to be interior\n",
    "            if lakeTracts == [0]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if y > 34:  #m==99940 or m==999909: \n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "greatLakeTracts = [893,1436,1472]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    isSkippedTract[t]==1\n",
    "    plotPoly(tractGeom[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1505 0\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VTDID</th>\n",
       "      <th>PCTNAME</th>\n",
       "      <th>COUNTYNAME</th>\n",
       "      <th>COUNTYFIPS</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREAFUE</th>\n",
       "      <th>G20PREPLAR</th>\n",
       "      <th>G20PRESKEN</th>\n",
       "      <th>G20PREIWES</th>\n",
       "      <th>G20PREIPIE</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>G20USSDSMI</th>\n",
       "      <th>G20USSRLEW</th>\n",
       "      <th>G20USSMOCO</th>\n",
       "      <th>G20USSCSTE</th>\n",
       "      <th>G20USSOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271730045</td>\n",
       "      <td>Friendship Twp</td>\n",
       "      <td>Yellow Medicine</td>\n",
       "      <td>173</td>\n",
       "      <td>26</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-95.72666 44.80511, -95.72658 44.790...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>270910110</td>\n",
       "      <td>Galena Twp</td>\n",
       "      <td>Martin</td>\n",
       "      <td>091</td>\n",
       "      <td>40</td>\n",
       "      <td>91</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-94.70471 43.84815, -94.70470 43.846...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>270930045</td>\n",
       "      <td>Darwin Twp</td>\n",
       "      <td>Meeker</td>\n",
       "      <td>093</td>\n",
       "      <td>129</td>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>291</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-94.37896 45.15235, -94.37891 45.137...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271370060</td>\n",
       "      <td>Biwabik Twp</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>137</td>\n",
       "      <td>249</td>\n",
       "      <td>335</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>316</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>MULTIPOLYGON (((-92.41044 47.49827, -92.41296 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>270010015</td>\n",
       "      <td>Ball Bluff Twp</td>\n",
       "      <td>Aitkin</td>\n",
       "      <td>001</td>\n",
       "      <td>75</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>95</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-93.19114 47.02518, -93.19113 47.025...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VTDID         PCTNAME       COUNTYNAME COUNTYFIPS  G20PREDBID  \\\n",
       "0  271730045  Friendship Twp  Yellow Medicine        173          26   \n",
       "1  270910110      Galena Twp           Martin        091          40   \n",
       "2  270930045      Darwin Twp           Meeker        093         129   \n",
       "3  271370060     Biwabik Twp        St. Louis        137         249   \n",
       "4  270010015  Ball Bluff Twp           Aitkin        001          75   \n",
       "\n",
       "   G20PRERTRU  G20PRELJOR  G20PREGHAW  G20PREAFUE  G20PREPLAR  G20PRESKEN  \\\n",
       "0          96           1           2           1           0           0   \n",
       "1          91           2           0           0           0           0   \n",
       "2         310           1           0           2           0           0   \n",
       "3         335           1           1           1           0           0   \n",
       "4         107           3           0           0           0           0   \n",
       "\n",
       "   G20PREIWES  G20PREIPIE  G20PREOWRI  G20USSDSMI  G20USSRLEW  G20USSMOCO  \\\n",
       "0           0           1           0          27          95           4   \n",
       "1           0           0           0          46          80           4   \n",
       "2           0           1           0         131         291          12   \n",
       "3           4           1           1         240         316          19   \n",
       "4           0           0           0          79          95           6   \n",
       "\n",
       "   G20USSCSTE  G20USSOWRI                                           geometry  \n",
       "0           2           0  POLYGON ((-95.72666 44.80511, -95.72658 44.790...  \n",
       "1           3           0  POLYGON ((-94.70471 43.84815, -94.70470 43.846...  \n",
       "2           3           0  POLYGON ((-94.37896 45.15235, -94.37891 45.137...  \n",
       "3          11           0  MULTIPOLYGON (((-92.41044 47.49827, -92.41296 ...  \n",
       "4           1           0  POLYGON ((-93.19114 47.02518, -93.19113 47.025...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/mn_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4110 0.46360486351433333 3201142 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic MN map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-92.,46)\n",
    "pt2 = Point(-89,46)\n",
    "pt3 = Point(-89,49.1)\n",
    "pt4 = Point(-93.6,49.1)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  76 tracts w pop 193125.0  to reassign to tracts...\n",
      "[1, 3, 5, 297, 483, 619, 855, 857, 875, 891, 1389, 1390]\n",
      "I have flagged  170 precincts to reassign to precincts...\n",
      "[228, 270, 410, 605, 657, 714, 847, 1203, 1210, 1345, 1873, 1931, 2112, 2192, 2578, 2763, 2764, 2765, 2766, 3966, 3967, 3968, 3969, 3970, 4022, 4024]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  193125.0 people (VAP of 156591.0 ) and  115663.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30cc0163-44fb-44f6-bbc4-37c630c65fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#special for Minnesota -- trim tract 639 that sticks into Canada\n",
    "goofyTract = 639\n",
    "t = goofyTract\n",
    "x,y = tractGeom[t].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-95.32,48.5)\n",
    "pt2 = Point(-94.8,48.5)\n",
    "pt3 = Point(-94.7,48.84)\n",
    "pt4 = Point(-95.32,49)\n",
    "trimPoly = Polygon([pt1,pt2,pt3,pt4])  #plan to clip off NE corner near Duluth\n",
    "xc,yc = trimPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e9df779-4907-408b-8a9c-640b80ca65c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#finish and display the trim\n",
    "tractMAP = tractMAP.difference(tractGeom[goofyTract])\n",
    "tractGeom[goofyTract] = tractGeom[goofyTract].intersection(trimPoly)\n",
    "t = goofyTract\n",
    "x,y = tractGeom[t].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "tractMAP = tractMAP.union(tractGeom[goofyTract])\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for SE corner\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-92.5,45)\n",
    "pt2 = Point(-92.7,43)\n",
    "pt3 = Point(-91,43)\n",
    "pt4 = Point(-91,45)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  67 tracts w pop 280918.0  to reassign to tracts...\n",
      "[654, 713, 716, 717, 1215, 1420]\n",
      "I have flagged  244 precincts to reassign to precincts...\n",
      "[107, 133, 670, 814, 1222, 1225, 1289, 1290, 1468, 1949, 2047, 2138, 2199, 2483, 2614, 2661, 2690, 3159, 3167, 4055]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.15:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  280918.0 people (VAP of 218091.0 ) and  154411.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8EdmulFpSy7me7j0LmAWQnJzcqNiuFTHBW8KLJD2nmLjwQDak5fDGkn0Uljn45/lDapifSh1OLn51BVsy8rAI3DOtXy1X1ATbrXx750Q+Wp3Kb3uP8tHq1Mq6qQPqTkajab9UKABtAqqdBs39lVI5IrIImK6UegY4E0BE+gHn1NImw3zPFJEvgNEYpqMjIhKvlDokIvEYs4tmoSJ2fnObgNYePM5F/11BoM1CqcNVWf77V1fw8c1j6RN7wlSRXVDGlow8xvTsyKPnDfbb9Ql/ISrUzq2n9ebW03ozc/ZKVu49xpe3T2BQlwhfi6bxU7QJqH7qNYqISIw58kdEgoEzgO3miB4RsQCPYHgEVW8bKiLhFZ8xFMZms/pr4Brz8zXAVyf1TerAafbFzR0OenemsVRS6nDR37RLR4fZOVpYxhnPLeGFn3dS5nCReqyIa99aBRihkQfG606sIQQHWClzurj27VWUlOt47xrPhGoTUL14MwOIB+aIiBVDYcxVSn0rIneJyO3mOZ8DbwOISBfgTaXUDCAOw2RUca8PlFLzzDZPAnNF5HrgIMY6QrPgaqE1gPVmrttpg+J44+pkAApKHfzv1wMs353NCz/vYt7mw0zqF8OuzAKuGJ3AafXk09XUpGLnsk72pKkLu81CoM2iw0HUgTdeQBuBUzyUvwi86KE8A5hhft4LDK/lukeB0xsob6NwmT1Fc84AlFLkmh3Tn6aeiKQZFmirNF38tOUwj3y5mVlm2GPt0tg4ukUFs2o/zBzbo9ZdzhoN6HhA9eFnfjHNQ8VIsTnjgbz32wG+23SIPrFhDO3medfJmYM789UdE4gOsyMCI7pHNZs8bZWSciefr0sHjExm1VNmajTuhAVpBVAX7UIBVLqBNkP/n1dSTl5JOX8zk5xP7hdT5/nxkcEse3Aqax+ZxuieOrJlQwm0Wbgs2fA4fvrHHUx5ZhEvLdjlY6k0/kqoXYeErgv/3QHUhFSuATSxCai4zMmp/15IbrFp+jm9L/d64c4ZFGDVpotGIiL8+/fDuGNqHzJyinlj6V7+s3A3fxjbo0kypGnaFnoGUDftYgbQXGsAy3dnV3b+0WGBXD2uR5NeX1M7CR1DGNOrEw9MH0CZw8XHbnsDNJoKwvUaQJ20DwXQxGsADqeLT9ekccO7KQCc1j+Gt68dVSOhiab56RcXzqjEKL5an+5rUTR+SKhWAHXSrkxATTUBeODTjZULkQD3n9WfwV10uEFfsCUjlx2H8ylxuFBK6ZwBmiqEBdn0RrA6aB8KoAlNQAePFvHF+nREjFj7PTqGVG760rQ87644QF6JgycuGqo7f00NwgNt5OtF4FppHwqgCUxADqeLN5ft48kftmOzCMsenKrTE/oBk/vH8HFKKnER2vymqUlooI1Sh4typ4sAa7uweDeIdqEATtYN9LmfdvDSL7srj++Z1k93/n7CyB7GXordmQVMHRDnY2k0/oZ7PCB/j3zrC9qFAjhZE9DmDCPR2V9nDGTmOL371F9wOF3c+eE6RGBUot5ToalJZVKYEq0APNEuFMCh3BKARvmJr9iTzS/bM4kOs3PjpF5NLZrmJNh/tJBV+45xat9okhI6+FocjR9SOQMo0+sAnmgXRrFShxExsqGJoYvKHNw3dwMA5w3v2uRyaU6OXtFhzBjamaW7srnwvytYsjOLA0cLWb47m3eW76Pc6ar/Ipo2jU4KUzftYgbgdCksYmSXagj/+/UAGbklvPaHEUwfEt9M0mkai8UivHLlCD5dk8a/vt3K1WaI7QoWbM/0mIxH037QeYHrpl0oAIdLYfMyFnSZw8Uv2zNxuhTLdmfTNzZMd/5+jIhwSXIC04d0JuXAcQ5kF1LicBEcYOX/vt/GlGcWkdwjivOSujAwPoLs/FKW7Mrm3OHxLZqCU+MbdFKYumkXCsDpUl65gG5IzeG299eSnlNcWXbdhJ7NKZqmiQgPCmBK/1jof6Js+pDOfL42nU/WpPJ3M1hfBdsO5fHl7VoBtHW0Cahu2oUCcDgVtnoUgMPp4vxXlgPw2HmDCbZb2ZNZwLUTEltAQk1zEBcRxK2n9ebmSb3YmZnP4dwSAm1WvliXxrzNh/XO4XZAZVYwPQPwSLtQAC6loI6/85yiMl42/fxP7RvNNeMTW0YwTYtgsQgDOkcwoLORevNIXglzU9J4e/l+rpuoZ3htmTCtAOrEm5zAQSKySkQ2iMgWEXnMLB8uIr+KyCYR+UZEaiS2FZEEEVkoItvMtne51T0qIukist58zWjar3YCq0Uq9wJU55OUVJL+OZ/Zy/Zxat9o3r52VHOJofETzk/qwpT+MTz14/bKNJ6atonVIoTYrdoEVAverIyWAlOVUsOBJGC6iIwF3gQeUkoNBb4A7vfQ1gH8WSk1EBgL3C4ig9zqn1dKJZmv70/mi9SFAJ66//3Zhdz/6UYAvrx9Au9eNxqb3i7e5hERHvndICKCArjov8v5v++3caywzNdiaZoJHRG0durt7ZRBgXkYYL4UxnLbErN8PnCxh7aHlFJrzc/5wDagxR3qRUBV0wAul+K6d1YDMPfmcSQldND24HZE75gwfv7zZC4f3Z1ZS/aS/Ph87v9kg9470AbROQFqx6vhrohYRWQ9kAnMV0qtBDYD55mnXAIk1HONRIzk8ivdiu8QkY0i8paIeEyQKyI3iUiKiKRkZWV5I66na6Dc5gBKKSY/s5C92YVMHRCrUzO2UyKCAvi/C4fy7Z0TuXpcIp+sSePvX22u1VyoaZ3orGC145UCUEo5lVJJQDdgtIgMAa7DMOmsAcKBWufQIhIGfAbcrZTKM4tfBXpjmJUOAc/Wcu9ZSqlkpVRyTEzd+XZrvT9VZwDfbzpM6rFihneL5I2rkxt1TU3bYUjXSB49bzC3T+nNh6tS+fvXm30tkqYJCbXrnAC10SCDt1IqB1gETFdKbVdKnamUGgl8COzx1EZEAjA6//eVUp+7XeuIqVhcwBvA6MZ9BS9ws+yk7D/G7R+sBeA/V45osixhmtbPfWf259S+0bz320HKHNoU1FYIC9I5AWrDGy+gGBHpYH4OBs4AtotIrFlmAR4BXvPQVoDZwDal1HPV6ty3116IYVJqNkodLv704Tp+/9qvALxxdTIJHUOa85aaVsbe7EJW7DnKtEFx2G3aGaCtoNcAasebX3k8sFBENgKrMdYAvgWuEJGdwHYgA3gbQES6iEiFR88EYCYw1YO751OmC+lGYApwT9N9rap0MqOAfr0hg7OHdGbNI2cwbZCOHa+pytvL92GzCE9cNNTXomiakNBAbQKqjXo3gimlNmIs3lYvfxF40UN5BjDD/LyMWrZgKaVmNlTYxnLDxF70ig4j9XgRf9ShHTQeOJJXwpaMPBI6hhAdprOLtSUqFoH1zu+atIudwBaLcIYe8Ws8oJTiwc82MjclDYC//W5QPS00rY2wQBvlTkWpw6WTOVWjXSgAjaY2Fu3IYm5KGpclJ3DDqT3pGxfua5E0TYx7RFCtAKqiFYCmXfPR6oPEhgfy+IVDdNLwNop7PKBO2rxXBf2L17RblFKsOZDDqX1jdOffhqmICKpdQWuif/WadsvRwjKyC0rp3znM16JompHwIJ0Upja0AtC0Wyr8QQJt2i7cltEhoWtHKwBNuyUsyIbdZmHVvmMUlenOoa2ik8LUjlYAmnZLoM3KzLE9+G7TIcb83wJeWbgbVT1srKbVU2EC0gqgJloBaNo1j5wzkE9vGcfIHlE8/eMOFu7I9LVImiZG5wWuHa0ANO0aESE5sSOzZibTKzqUf327jZJyp6/F0jQhIXYrInoR2BNaAWg0gN1m4b6z+rMvu5BftutZQFtCRAiz28jXCqAGeiOYRmNyxsA4YsIDefKH7WTkFHNa/xh6x4Tp+DFtgLAgmzYBeUArAI3GxG6z8PylSTz2zRYe/24bj3+3jY6hdgbGh9MlMphgu5WiMid2m4VbJ/fW4cRbEaGBNgq1p1cNtALQaNyY2Dea+fdOJu14EYt3ZrExNZeth/LYk5lNQamDyOAA0nOK+WpdOpckJ3D56AQGdI7wtdiaeggL1ElhPKEVgEbjgW5RIVw1pgdXjalZd/BoEY9+s4UPVh7k/ZUHeOGyUzhnWHzNEzV+Q5hOCuMRvQis0TSQ7p1CeOvaUax8+HSSEjpw54drefzbrezOLPC1aJpaCNNJYTziTUrIIBFZJSIbRGSLiDxmlg8XkV/NrF7fiIjHebCITBeRHSKyW0QecivvKCLzRWSX+R7VdF9Lo2l+okLtzLluNBckdWX28n1Mf2EJy3Zl+1osjQf0IrBnvJkBlAJTlVLDgSRguoiMBd4EHlJKDQW+AO6v3lBErMArwNnAIIw0khUZNx4CFiil+gILzGONplURYrfx3GVJLH9wKg6X4udtR3wtksYDYYHaDdQT9SoAZVAxtw0wXwroDywxy+cDF3toPhrYrZTaq5QqAz4CzjfrzgfmmJ/nABc05gtoNP7Akp1ZAEzuH+NjSTSeqDAB6VAfVfFqDUBErCKyHsjESAq/EtgMnGeecgmQ4KFpVyDV7TjNLAOIU0odAjDfYxssvUbjJ6QeLwJgZI+GWTJdLsVT87Yzd3Vq/SdrGk1YkA2XgmK9y7sKXikApZRTKZUEdANGi8gQ4DrgdhFZA4QDZR6aetpB0yAVLCI3iUiKiKRkZWU1pKlG02KcNbgzAJ+auYW95dn5O/jvoj088NlG5m0+1ByiaXCLCKrXAarQIC8gpVQOsAiYrpTarpQ6Uyk1EvgQ2OOhSRpVZwbdgAzz8xERiQcw3z3uv1dKzVJKJSulkmNi9PRa458M69aBoV0j+ee3W3nky01euRzOXZ3KKwv3cNGIrpzSvQN3f7yejWk5zS9sOyRch4T2iDdeQDEi0sH8HAycAWwXkVizzAI8ArzmoflqoK+I9BQRO3A58LVZ9zVwjfn5GuCrk/geGo3P+e9VIwB477eDDPnHjwx/7CdG/Gs+F7yynL9/tZncovLKc5ftyubhLzYxqV8MT108jFkzk4kOC+T6OSlk5BT76iu0WXRSGM94MwOIBxaKyEaMDn2+UupbDI+encB2jFH92wAi0kVEvgdQSjmAO4AfgW3AXKXUFvO6TwLTRGQXMM081mhaLQkdQ9j3xAzev2EM145PpFdMKGN7dcRus/Durwf400frUEqx80g+t763hj6xYbxy5SnYrBZiwgN569pRlJQ5ue6d1bqjamJ0UhjPSGtaFU9OTlYpKSm+FkOjaTDvLN/Ho99s5enfD+OFn3dR7nTx5e0T6NIhuMp5S3Zm8cd3VjOpbzRvXJ2MTSerbxI2p+fyu5eXMWvmSM4012vaEyKyRimVXL1c/7o0mhbgyjE9CA+0cf+nGzlaWMpb146q0fkDTOoXwz/PH8zCHVk8/t02H0jaNtEzAM9oBaDRtAB2m4Wnfj+MDiEB/OeKEQzpGlnruVeN6cENE3vyzor9zFmxv+WEbMNUrAHocBBV0cHgNJoW4uyh8Zw1uDMWS/35Bf4yYyD7jxbx2Ddb6N4xhCkD9DaZk6EiL7DeDVwVPQPQaFoQbzp/AKtFePHyJAZ0juCOD9ay7VBeM0vWtgm0WbBaRO8DqIZWABqNnxIaaGP2tcmEBdm4/p3VZOaV+FqkVouI6IigHtAKQKPxY+Ijg3ngrAFk5Jbw4SodLuJk0AHhaqLXADQaP+Zwbgn/nredrh2CuWpsd1+L4xXpOcX87cvNbEjN4YyBcTx58VC/yKscFqhDQldHKwCNxk8pKnNww7urKSx18Nlt44kOC/S1SPWy9uBxbno3haIyJ0VlTj5OSWVkjyguHeUpVmTLEhak8wJXR5uANBo/xOVS3PvxBrZm5PHylae0irzDy3dnc8Ws3wix2xjds2Nl+b++20pmvu/XL/QMoCZaAWg0fsiz83cwb8thHp4xkKkD4nwtTr1sycjlhjkpJHYK5bJRCSzacSJyb36Jg1+2eYz12KLoNYCaaAWg0fgZn69N45WFe7hidALXT+zpa3G84onvtxNst/LKVafw8i+76B0TWlkXaLNUmRH4Cu0FVBOtADQaPyJl/zEe+mwTY3t15LHzhvjF4ml9rDlwjGW7szl7SGd+9/IySspdvHLVCM4d3oXgACtPXDSUXjFhvhaTUG0CqoFeBNZo/ITUY0Xc/L81dOkQxGt/GInd5v/js7ySch75cgsdQ+0UlTlxueC1P4xgQOcIXrwsCYWxqc0fMBaBnbhcyusNeW0drQA0Gj8gv6ScG+akUO50MfvaUXQIsftapCrkl5Tz3cZDHC0sI7e4nHKni06hdj5dk0ba8WJmXzuKhz/fxLRBcUwfEg94v+u5pahIClNY5iA8KMDH0vgHWgFoND7G6VLc9dF6dmcVMOePo+ntB+aS6ry/8iBP/rAdgKAACxYRisqc9IkN470bxjCoSwTpOcVcOcZ/9yq4RwTVCsBAKwCNxse8/MsuftmeyeMXDGFi32hfi+ORDak5dO8Ywk/3TCIowEpBqYOF2zPp3zmcPjFhzF62D8AvFntrIyxIRwStjlYAGo2PWbHnKEkJHfjD2B6+FqVWNqXnMjyhA0EBVnKKyrj41RXsySoEDNNKfqmD5B5RjEr0XwVQYQLK1wvBlXiTEzhIRFaJyAYR2SIij5nlSSLym4isF5EUERntoW1/s77ilScid5t1j4pIulvdjCb/dhpNK6C4zElUiP+aJI4XlpF2vJhhZg6DN5buZV92Ic9eMpxnLhnOeUldSO4Rxf1n9fexpHWjk8LUxJsZQCkwVSlVICIBwDIR+QH4J/CYUuoHs/N+CjjNvaFSageQBCAiViAd+MLtlOeVUs+c9LfQaFoxRWUOQuwhvhajVjal5wIw1FQA+7ILSewUysUjuwHwe/Pd39FJYWpS7wxAGRSYhwHmS5mviv3pkRiJ4evidGCPUupAI2XVaNokRWVOgu1WX4tRKxUKYLCpALpEBpN2vLjVjaQrk8JoE1AlXjkai4hVRNYDmcB8pdRK4G7gaRFJBZ4B/lLPZS4HPqxWdoeIbBSRt0QkqpZ732SamFKysrI8naLRtGqKypyE+LMCSMslsVMIkcGGmWraoDjKnC4W7ag/vEO509Xc4nmNNgHVxCsFoJRyKqWSgG7AaBEZAtwK3KOUSgDuAWbX1l5E7MB5wCduxa8CvTFMRIeAZ2u59yylVLJSKjkmJsYbcTWaVoNSiqIyBzaL/2762pyRWyWHcXJiR2LCA7l37gZufDeF9ak5AOzNKuDXPUcBI5jdbe+voe9ff+CHTYd8IXYNQgMNJatNQCdo0K9OKZUDLAKmA9cAn5tVnwA1FoHdOBtYq5Q64natI6ZicQFv1NNeo2mTiAhWi2D10/6/qMxB2vFi+seFV5ZZLcKHN47hqjHd+W3PUS5+dQXzNh/mjOcWc8Ubv5GRU8w7K/bz/abDACzfk+0r8asQaLNit1l0QDg36l0EFpEYoFwplSMiwcAZwL8xbP6TMRTCVGBXHZe5gmrmHxGJV0pVDA0uBDY3WHqNppWyO7OAlxbsol9cGCXlLmLC/SPWf2Z+Cc/9tBOAoAArpQ7DhFM9lk+f2HD+ce5gRvaI4o4P1vGfhbtwKaPutvfXcsQtfeXEPv4zc9choavijRdQPDDH9OKxAHOVUt+KSA7woojYgBLgJgAR6QK8qZSaYR6HANOAm6td9ykRScJYTN7voV6jabPc/L+USj/6ntGhXDTCPzxpft1zlI9Wp9Ip1E6500VJuYtQu5Vh3SI9np96rBiA9OPFTO4XQ++YMD5dk0peiYPI4ADySspZl3qc6UM6t+TXqBUdEbQq9SoApdRG4BQP5cuAkR7KM4AZbsdFQCcP581sqLAaTVtgT1YBe7IKsVmExQ9MITY8kAA/sQE5zWH8Z7eOJzHaCOmslKo1KmmRmWGrf+dwdmcW8PrMkUzuH8M1b61iQOdwukYF8/rivfSPC/cLJRcaaNOLwG74x69Oo2knLN+dzfQXlgBwWv8YukQG+U3nDycUgHsEz7pCUucVlxMeaOOacYmk5xTz696jTOobzee3jee/V43gwekDCLVb+c/C3c0uuzeEawVQBR0KQqNpIYrKHPzl803Ehgfx+W3jiYsI8rVItaKUd+fFRgSRX+pgT1aB2c6YLYzofsKr+7YpfXj6xx3879f97D9axJmD4hjTq4ZRoEUIC7L5RXpKf8F/hh4aTRtnfWoOB48Vcdfpff228482F6OzCrzrJMf3NjryZ37ayYjuHRjfu2Ywuz9OSGRgfAR/+2oLs5ft4+b31rA/u7DphG4AOilMVbQC0GhaiAgzBHFEsP/G/elsKqbDuaVenZ+U0IH7z+rP/Wf15+ObxxEUUHNDW4jdxoc3juHOqX2YNdNYNrx+zmpKyp1NJ7iXhAXaKCht+fv6K9oEpNG0EF07BANUmkv8kUoFkOfdDEBEuH1Kn3rP6xBi589nGsHiXEpxy3trWbnvGJP7tayLaHiQjYLS8ha9pz+jZwAaTQsRFWqnc0QQ+3xk/vCGDiEBRATZ2JqR1+hrlDtdlDqcKKUoKHWQnlNcWbc5PZeXFhgLwgu2HantEs1GqN1GSbkLhx+FqPAlegag0dSFUpB/CCwBYLODLQisdmhEsvbN6blkFZT6dehnEWHKgFgW76w/zo8ndh3J5/xXllNUdsLMUuHuuvbAce77ZEOlCcwXQdlOJIVxEhmix79aAWg0dbH0Gfjl8Zrl1kBDGdgC3V6mcnArV7ZAHGLnaAls3ZvPv4LtzHAlwuJw4xyrW1ubW1tvrm9pngByQ7tG8tX6DDLzSoht4GK1mKki4yICuWxUd9an5rBkZxYPfbaRpbuyGZUYxatmwvtQe8t3P5VJYUrLifRjRdxSaAWg0dRGSR6seBl6TITBF4CzDBwl4Ch1e5V4KC+BskIKc7LIPJ6LxVmKXRycKeVEiBPLujJwNcHo12KrpjDqUxqelEx15RPEmJJiTrMcIGcLxHbtVPM6AcEQHOVxFtQnNozeMaF0jgzi3mn9KCpz8Oe5G5i35TBnDorj5StPIdDmu8inOiJoVbQC0GhqY9UsKMmFsx6HLjU2w9dJYamDM55bjDVImDm2BxHBAZw7vAsWswPC6QBnNUXiKHUrKwFHhWLxpGSqKx/3892uU5pftdy9jbPMo+xDgXfswI91fMEpj8Dk+z1WTewTzXsrD5JbXE5kcACv/mEkxwvLiAwOwGJpuOmsKdF5gauiFYBG44nSAvj1Feh7ZoM7f4AXF+ziUG4Jn94yjmRPeXKtNuNlD20CYRuJy1VVsZhKY86S7Xy5Zh+fXH8KNldZTeWz7Dk4uMLjJb9an84Hqw7SMzqUAOuJzj4q1N5S36pOwsyQ0DopjIFWABqNJ1LeguJjMMnzKLcuth/OY/ayfVyWnOC58/cXLBawBEFAVTv/QXs5u2x2bL1O9dzuwArYt6RG8cIdmdw7dwOjEqN4fWYyIT6w8ddHWKBh99cmIAO9DK7RVKe82LD995wMCQ1LU3Ekr4R7Pt5ARJCNh84e0EwCNi/lThd2Wx1dQ0x/yM8wzGMmJeVObno3BadLcWlyQmX2MH9Dm4CqohWARlOdNXOgMBMmP9CgZgePFnH6s4vZdiiPh2cM9BuzR0Mpc7iqmG9qEDvQeM/aUVlktUjliP/euRv4bqN/ZAGrTphd5wV2RysAjcYdRyksfxG6j4fEiQ1qev+nGxDggxvHcElyQvPI1wKUOV11RyiNMWc2mdsqiwKsFj65ZRyf3zaeXtGhvPfbgWaWsnGcSAupw0GAXgPQaKqy7j3DvHHBKw1qlpFTzMp9x/jztH4eA6K1Jsqdqm4F0KEH2IKrzAAA+plpIzuG2inz0522NquF4AAr+SU6HAToGYBGcwJnOSx7AbomQ68pXjdTSvHgZxsJCrAwY1h888nXQgQHWDheVIbLVUtMaIsFYvpBljEDWL47m5cX7CLP7FQPHiuid4wPvZvqITTQRmGZNgGBFwpARIJEZJWIbBCRLSLymFmeJCK/ich6EUkREY+rZSKyX0Q2VZznVt5RROaLyC7zPcpTe42mxdj4MeQeNGz/DQj1sONIPkt3ZfPnaf3pXS13bmtkTM9O5BSVs+NIfu0nxQyEzO0cLSjlqjdX8uz8nazedwyAvJJywoP8cxEYDFdQHRHUwJsZQCkwVSk1HEgCpovIWOAp4DGlVBLwd/O4NqYopZKUUsluZQ8BC5RSfYEF5rFG4xucDlj6LMQPN3z/G8DiHVkAnDu8S3NI1uKM6WW4rq7ce7T2k0xPoOL845VFQQFWth/OIyjAStrxouYWs9GEBem8wBV4kxNYARXxawPMlzJfEWZ5JJDRwHufD5xmfp4DLAIebOA1NJqmYcvncGwvXPZegwO9LdmVRf+4cDpH+meSl4bSLSqEblHBrNhzlGsn9PR8kukJ1LFoX2XRVW+uBCDAKgzr1qG5xWw0oXadFKYCrxaBRcQKrAH6AK8opVaKyN3AjyLyDMZMYnwtzRXwk4go4HWl1CyzPE4pdQhAKXVIRGJrufdNwE0A3bt39+5baTQNweWCJc9A7CDof06DmhaVOVi97zjXjO/RTML5hmmD4vjfrwdIPVZEQseQmieYnkAhOTvpE5vI7swCrhidQNcOwfxhbA86hPivC2x4kI2MHJ0WErxcBFZKOU1TTzdgtIgMAW4F7lFKJQD3ALNraT5BKTUCOBu4XUQmNURApdQspVSyUio5JqZlk0do2gnbvoLsHTDpPmOBswFsTs+jzOmqkgO3LXDzpN5YRHhpwS7PJ7h5AvWOCaVXdChPXDSMO6b29evOH/QisDsN+rUrpXIwTDXTgWuAz82qTwCPi8BKqQzzPRP4wu28IyISD2C+Ny4AuUZzMlSM/jv1hUEXNLh5/86G6+OKPXXYy1shnSODuHZCIp+sSWPWkj01T3DzBOrRKZTU40U+SfHYGMJ0XuBK6jUBiUgMUK6UyhGRYOAM4N8YNv/JGAphKlBjqCAioYBFKZVvfj4T+KdZ/TWGEnnSfP/qpL+NRtNQdv4ARzbDBa81Kr5+RJANESN8QlvjvjP7k3qsiCd+2M6AzhFMqp6+MWYg7FvC5HExzFqyl7kpqVw9LtEnsjaEsEAbOcXl3Dt3PSF2K6F2GyF2G6GBVoIrj62EBtqqHIfYrdhtFgKsFqwWwWYRpBGJgfwJb9YA4oE55jqABZirlPpWRHKAF0XEBpRg2ulFpAvwplJqBhAHfGE+JBvwgVJqnnndJ4G5InI9cBC4pOm+lkbjBUrB4qcgKhGGNu7nJyKMSuzI8j3ZTSubH2C3WXj20uHseaWAuz5ax28Pn141ln9Mf9j4EeO72hjbqyNPz9vB6QPjKnMf+yun9o1h0Y4sVu49RnG5k8JSB6WOxilwm0WwWYUAiwWbVbBZLZVlVhEsImD8Q0TMdxCk0tegSrlbXUU5GDuYn780qcEJeuqVv74TlFIbgRrxcJVSy4CRHsozgBnm573A8FquexQ4vYHyajRNx+6f4dB6OPclIzRzI+nRMYRNabkopRo8IlRK8fnadL7akEFSQgfuOaOvX40qQ+w27juzPzf9bw3Pzd/JQ9MHnJDP9ASS7J08/fvhTH9hCX/5fBPvXtewAHotzcS+0fx4T9WlSIfTRVG5k+IyQyEUVbyXOykqdVJY5qCo1EG5U1HucuFwKhwuhcPpwuFSlDsryk7UOV0KBbiUAgUKhVLGuKPyM8YxVY4r2hmyHS0oZfnuHPYfLWp5BaDRtEkqRv+RCTD8ipO61MgeUXyyJo21B48zsof34Z+VUjz+3TZmL9tHdJidJTuzGNOzIxP6+FcoiWmD4rg0uRuvL95Lv9hwLh7ZzahwiwmUMHI0t0/tw1PzdrAvu5Ce0f67E9gTNquFCKuFCD/cwPbFujTu+XgD0WFNv7iuQ0Fo2if7FkPaKphwl5EO8SQICjDMIg0dub+4YBezl+3j2vGJLH1gKh1D7byzYv9JydIciAhPXjSMHp1CeOrH7Sc2eVV6Am0H4KJTumGzCI9/u7X2MBKaBnMkrxSA6PDAJr+2VgCa9snipyGsM5wy86QvtWr/MYIDrCQ1YPPTNxsyeOHnXfx+ZDf+ce4ggu1WLjqlKwu3Z1Jc5n/eNBaL8NofRlJU6uQvn2+qKDQ9gQwF0DkyiL/MGMiC7Zks3d321kR8xYGjhXQMtTfL7EQrAE3748AKOLDMGP0HnLxNtV9sGMXlTvZmF9Z53ub0XK57ZzVXv7WKv3y+iYHxETx50dDKmcPk/jE4XIqfth4+aZmag4HxEdxwai+W7spmeUUHb8YEquCK0UYY7M3puZ4uoWkEe7Oaz6SmFYCm/bHkaQiNgZHXNsnlpg8xIoD+vO1Iree4XIpnftrBL9szyS0qY3TPjrx0eRI2t7DLE/tEExcRyPyttV/H19w0qRe9Y0K55+P1fL42DVWRHaw4p8p5Fj9ayG7NOF2KrRl5laG2mxqtADTti7Q1sOcXGHcH2D2EOGgEnSODiA6zs+Ow5+iZuUXlPPjZRhbtyOKvMwby1R0TeevaUfSt9kctIkzoHc2KPUf91oYebLfynytHkJlfyr1zN3AkyIwVlL0TMDKD2a0WdhzO86GUbYedR/LJL3WQ3KN5dpprBaBpXyx5CoKjYNT1TXrZqQNi+XpDBnNTUqt03vuyC7nwv8v5fF06N03qxQ2n1hJczWRMr44cKyzj4DH/jaY5MD6CPrFhdO0QTFSPYUahmR0s0GbljEHGs9hXj0lMUz8pB4xoq8mJWgFoNCfHoQ2wcx6MvR0Cm3ZK/fdzBzOyRxQPfLqRG99NIT2nmNnL9nHuy8s4XlTGhzeO5eEZA+v1FLKasYjy/DxjVanDyaAuEQRG96ziCQTGDuJQu41LXvuVTWl6LeBkWLP/GDHhgXT3FJCvCdAKQNN+WPI0BEbA6Bub/NJhgTY+vHEs/zh3EAt3ZDLhyV/417dbGdI1gm/unMjont7tD5jQpxNWi/j1OkCpw0lpucvYwFTNEwigV0wYn902nuyCUm54dzVljdxlqzFmAMk9opptc6DeCKZpHxzZCtu+gUn3Q3CHZrmF1SL8cUJPhnSNZPX+Y5zaJ4ah3SIbdI34yGA6RwSx7mBOs8jYFHy48iCZ+aVcOz7RKDBjArkzx9zPULGz1n6Sey3aI0fySkg7XnziOTcDegagaR8sfQbsYTD2tma/1ajEjtx2Wp8Gd/4VhNitLNudzZ8+XEdOUVkTS3dyFJU5ePmX3Yzr1YkJfToZhR48gZbvzmZgfATLHppKVKju/BtDyv4K+7/3u8sbilYAmrZP9i7Y/Lmx8BvSfH9MTcWLl5/CZckJfL0hg4n/XuhX5qB5mw9ztLCMP53et0ZMoApPIIBh3TqQXVBKcEDDI6xq4M2le7n9g7UE2iwM7hJRf4NGohWApu2z9FmwBcG4O30tiVcM6hLBv38/jB/uOpVeMaHc/dE6jhf6x0xg9jIjBeRz83ewqyJpvFtMoArOHtKZrPxSZs5eyeFcnX2rIaTnFPPED8aayl1n9CXA2nzdtFYAmrbNsX2wcS4k/xHCWldGuYHxEfztd4MoLHOycp9/JJy5fFQCM4Z2ZldmATNeWsr3mw7ViAkEcPbQeJ67dDjrU3M48/nFfLuxoSnD2y+vLNyN1SIsf2gqt53Wp1nvpRWApm2z7Dmw2GD8n3wtSaOwmFaWYLt/+GvMHJfIf68ayYJ7JxMdFsiDn25EidTwBAK4aEQ3frhrEr1jw7jjg3V8uibNR1K3HrILSvlmfQZnDe7cInkVtALQtF1yUmH9hzBiJkTE+1qaRlGRgOXPczfw4xb/iRG0/2ghWfmljOnV0VgLqBYTqIKe0aF8fNM4hnaN5LXFe1DKP3c4+wtvLNlLYZmDu07v2yL3q1cBiEiQiKwSkQ0iskVEHjPLk0TkNxFZLyIpIlIjC4SIJIjIQhHZZra9y63uURFJN9uvF5EZTfvVNO2e5S8a7xPu9qkYJ8Og+AgemN6f6DA7N/9vDY98uemE7d2HPPnDdmLDA3n20iSjoJaYQGBkFrtyTHd2ZxawPrVmveYER/JKiI8Mpk9sWIvcz5sZQCkwVSk1HEgCpovIWOAp4DGlVBLwd/O4Og7gz0qpgcBY4HYRGeRW/7xSKsl8fX8S30OjqUr+YVj7LiRdAR0SfC1No7FYhNtO68NXd0zg2vGJfLDyIGe+sMSnNvX0nGJW7z/ONeMTiQw2QxR78ARy55xh8QQFWPhoVWoLSdn6KHe6+G3vsRZNplOvAlAGBeZhgPlS5qvCPykSI0l89baHlFJrzc/5wDagaxPIrdHUzfKXwOWAiff6WpImIdBm5dHzBrPy4TMY1aMj93y8/kRI5hYm3wxT0cXdRu3BE8idiKAALhmZwGdr03SMoFr4buMhDueVcN3ExBa7p1drACJiFZH1QCYwXym1ErgbeFpEUoFngL/Uc41EjNzCK92K7xCRjSLylog0T7QjTfujIAtS3oJhl0LHuoOvtTZiwgN54+pkekWHce3bqxj4t3lc985qjuS1nKtlQlQIFoFdmQUnCj14AlXnT6f3xW6z8P++29bu1wKW785m0lMLuXfuesBID/rG0r30jgnltH6xLSaHVwpAKeU0TT3dgNEiMgS4FbhHKZUA3APMrq29iIQBnwF3K6Uq4sS+CvTGMCsdAp6tpe1N5hpDSlZWlldfStPO+fU/4ChpM6P/6kSGBPD+jWM4tW8MxeVOftmeyV0frcPpIYR0SbmTY4VlTdrhhgbaGNA5gjUHjp0o9BATqDox4YH86fS+/LztCM/P30lBqYMF24741Ua35kYpxbu/7ueqN1dy8FgRGTnFAHy78RBbMvK4eXJvLJaWy6XQIN8ypVSOiCwCpgPXABWLup8Ab3pqIyIBGJ3/+0qpz92udcTtnDeAb2u55yxgFkBycnL7HjZo6qfoGKx+EwZfaHRIbZTosEDeunYUAHNTUnng043c/v5aHjp7AN07hvDpmjT+9tVmnC6Fw6UItVsZEB/BXaf3ZVi3SCKCAk6qo+neMYR5Ww5T5nBht5njSA8xgapzw8Se7Dicz0u/7OalX3YDEB5kY9OjZzValtbE3JRU/v7Vlspjp0tRXObkie+3MSg+gotHdGtReepVACISA5SbnX8wcAbwbwyb/2RgETAV2OWhrWDMDLYppZ6rVhevlDpkHl4IbD6J76HRGPz2KpQVwKT7fC1Ji3HJyG7kFZfzxA/bmbflMLHhgeSVlFPqcHHL5N5Eh9lJO17M/K1HuPqtVQB0iQzimvGJXD66+4mF3AZw4YiuzNtymK83ZPD7kWanFdMfNn5keALVEnDPZrXw3KXDuWxUAiv2HCVl/zHWHjzeyG/e+vhleyYAn906jj99uJ5OoYG8vmQPGbklPH9ZEtYWHP2DdzOAeGCOiFgxTEZzlVLfikgO8KKI2IAS4CYAEekCvKmUmgFMAGYCm8w1BICHTY+fp0QkCWMxeT9wc1N9KU07pSQXVr4OA34HcYN9LU2LISLccGovpg2K4/tNh/n3PMMM88/zB3P1uMTK8/4yYwDfbDjE8cIyftmeyRM/bOeVhbt569pRDQ44Nm1gHEO7RvLCzzs5d3i8sV/B3RMooYZXeBV5x/bqxNhenXjyh+2VSU/aOmnHi1i4PYuB8RGM6B5FTHggm9JzWbQzk3OGxTOmV6cWl6leBaCU2oixeFu9fBkw0kN5BjDD7RyPKk0pNbOhwmo0dbJyFpTmGiGf2yE9OoVy62m9mZuSyr7sQs4d1qVKfaDNWjlav3FSLzan5/KnD9dx9VurePOaZMb3jvb6XhaL8MD0/sycvYoPVh7kjxN6VvUEqkMBuBMeZKPM4aKozEGIn+x2bi5eWrCLMqeLWyb3QkSIDQ9kfWoOgTYLD88Y6BOZ9E5gTdugNB9+ewX6ngVdknwtjU/58rYJfHPHxHrDMA/pGslHN4+lW1Qw172zmi0ZDcveNbFPNON7d+I/v+ymoNThlSdQdSoyXbWHzGG5xYb77Jiexkg/NNBQeLdM7t0iYR88oRWApm2wejYUH4fJD/haEp8TGRLgdS6C2PAg3rthDKF2Gy8tqLGMVyciwgPTB3C0sIzZS/d55QlUnakDYrEILN3lmz0NLcn+7CIigmyVIbL/9rtBPHfpcG6b0ttnMmkFoGn9lBUZrp+9pkC3ZF9L0+qIDQ9iUr8Y1h3MabC7aFJCB84e0pnXFu8hPae41phAtREaaMMigqJtO/gdyi1mx5F8rh2fSGSIsejeMdTORSO6VcZ78gVaAWhaP2vnQGGWHv2fBCN6RJGZX8qerIL6T67GX88x7NePfr0FYgfUGhOoNlxKIZ6XCtsMecUOAF76ZTd//8p/HB61AtC0bspLjKBvPSZCj/G+lqbVctbgOKwW4bO16Q1u2y0qhFsm92b+1iNkBZk7r2uJCeQJxYmw122RNQeOc+5/llUepx4r8qE0VdEKQNO6Wf8e5B+Cye3T86epiA0PYlLfaL5Ym86KPdn8uudog8xBF480Qnz9mGVGdKklJpAn2npUiC/WpeFwuvjXBUO478x+PPK7QfU3aiHatt+Vpm3jKINlL0C3UdBzsq+lafVcmpzAre+v5co3jHBdN0/qxV+8dE8sdbgAyLbFNdgTKCjAQonZvjqHcourBI/rHxdOp7BAr6/ta5RSrD2QQ6+YMGaO7eFrcWqgFYCm9bLxI8hNhXOeA2nDNoQWYvqQztw5tQ/5JQ7ySxy8sXQvZw+NJymhQ71tn5u/k7BAG38Y1xP2NswTKDwooNacx1fPXlUl6FyAVXjp8lN46Zfd2G0Wnr1keIvFzm8MaceL2Xooj/vO9M+wJFoBaFonToeR7D0+CfpO87U0bQIR4c9n9geMkM8Ld2TyxpK9jOnVkZcW7CY6zM7ffjeICX1qbhjbm1XIiB5RRIcFehUTyJ2B8RFszsjzWNfB9Jj56Kax3PvxejJyS7j1/bWEBdooKHWw9uBxv1YAcRFBhNitpOe0XLTWhqDXADStk82fwvH9xq5fPfpvcsKDAjhnaDzfbTrE37/aQs/oEDLzS/nj26uZ8eJSvlxXdbF4UHwEm9JyjIMGegKN6dmR7Yfz2JxeczPYODM8wsgeUSx5YAqT+8UQFGDh/rMqFJWj0d+xOXlpwS56/uU7Jj+9kKIyJ1EhDY+31BJoBaBpfbicsOQZiB0M/XUm0ebitinGDtWpA2L54Max/O/60UwbFMfWQ3k8/eOOKuGnI4JtOCqOK0JCZO3w6j5XjelOdFgg932ygRvmrDbcSU0qI41iBJKbc91otv1zOleN6Q5AgR8qgKIyB8/N34lScCjXGPlP6hfjY6k8oxWApvWx9Us4usuI+GnRP+HmIj4ymGUPTmH2NckEWC0M7hLJK1eN4F8XDCE9p5h/fH3Cn91utVDqcBmeQ5UKwLt1gA4hdv7vwqFsP5zPz9syeWfFft5cuheAcmdNFyERwWa1EGK3VmYn8xcO55Zw+rOLa5RvO+TZxOVr9F+PpnXhchmj/+h+MOh8X0vT5hERpJqJbebYHlw5pjsfrkqtXLyNCrVT5nA1OibQtEFxXHiK4Uo6oHM4j3+3jVv+t4a3lu1jYHwENg8bBcKDbH5nArr1/TWVo353vljX8P0VLYFWAJrWxY7vIHMrnHofWHy3hb69c+Xo7jhdih+3HAagX5yxELs1I+9ETKAG7AUAeOKioXx26zi+uXMit57Wm5+3HUEEXrgsqYYSAioXgv2JUR7Cak/uF8O907QXkEZzcigFS56GqJ4w5GJfS9OuGdwlgh6dQnh/5UFG9oiib2w4AOtTc4y49g30BAIICrAysofRgT44fQB3Tu2DRYSggJqKvszhYk9WYdXE9H7AA2f158OVB8k3FVNcRCBzrvMuNLYv0DMATeth13w4tAFO/TNY9djFl4gI953Zn80ZuUx7fgkXvLIcixgKAGhUTCB31hw4zksLdvPApxtZte9YjfqNpseRw8MagS+xWS18dPPYyuP/XDnCh9LUj/4r0rQOlIIlT0Fkdxh+ua+l0QDnDu9CYqdQlu/J5skfDHt/Rcz7Kp5A3cc06LqvL97DEz9sJ8AqlDsVX2/I4IvbxnNK96jKc+IiggC44JQutV2mxfl56xGe/3knW8w9DT06hXg0CfkT9c4ARCRIRFaJyAYR2SIij5nlSSLym4isF5EUEfE4zxGR6SKyQ0R2i8hDbuUdRWS+iOwy36M8tddoANi7CNJWw8S7weqfPtXtkaHdIrnx1F70jgkFMDKDQYM9gSooKXfy/M87OX1ALOv/fibz7j6V7h1DuOjVFVz3zmqKygzTSnxkEFaLkHa8uMm+y8ny5LztlZ3/4xcM4b3rG6b4fIE3M4BSYKpSqkBEAoBlIvID8E/gMaXUDyIyA3gKOM29oZlH+BVgGpAGrBaRr5VSW4GHgAVKqSdNxfAQ8GBTfTFNG2PJ0xAeD0lX+VoSTTWsFuG7P51KVn4pCWaGr8Z4AgFsSs+lpNzFFaO7ExpoY0DnCD69dRzP/LiDuSlpLN99lGmD4rBZLcRHBrH9cH4zfKPaKSl3kp5TTOqxItKOF5uvIlKPF7M7s4BQu5WPbx7HkK7eJeTxNd7kBFZARTCOAPOlzFeEWR4JZHhoPhrYrZTaCyAiHwHnA1vN99PM8+YAi9AKQOOJ/cvhwHKY/iQEBPlaGo0HggKsJzp/aLQnkMtV06bfIdhemS/YPWbQ2UM6M3vZPg4eLaJ7p5Aa7RpDmcNFRk4xqceNDv5ER2908ln5pVXOD7AKXTsE0y0qhCtGJ3Dm4M6tpvMHL9cAzJH8GqAP8IpSaqWI3A38KCLPYJiSPAVj7wqkuh2nARXzojil1CEApdQhEYmt5d43ATcBdO/e3RtxNW2NJU9BaAyMuMbXkmgaQiM8gY4XGR38/qMnIoAeKyzjnRX7ARjV84RNfXK/WN5Yuo9V+4+dtAKYm5LKSwt2kZ5TXCU8tc0idOkQTLeoYKb0jyEhKoRuHY0Ov1tUMHHhQVhacTIDrxSAUsoJJIlIB+ALERmC0Snfo5T6TEQuBWYDZ1Rr6unJNGjZXik1C5gFkJyc7F9L/prmJ3W1Yf+f9k+wN80oT9NCxA4wIrYW50BwB6+adAw1Qj0P6BxRWZZdYIy6hyd0oGd0aGX5+ysPEBZoY+oAj2NHr1m4I5MHPt1Ij04h3Dm1LwlRRgef0DGYzhFB2Kxt11myQV5ASqkcEVkETAeuAe4yqz4B3vTQJA1IcDvuxglT0RERiTdH//FAZkNk0bQTljwFwR0h+XpfS6JpKI3wBAqxGz7/FYu9AP/3/TaiQgK4dXKvyrL8knLmbz3CHyck0jHU3mgRlVL88e3VADxw1gDOGRbf6Gu1RrzxAooxR/6ISDDGKH87RkdekYVjKrDLQ/PVQF8R6SkiduBy4Guz7msMJYL5/lUjv4OmrZKxDnb9BONug0D/DfmrqYVGeAKFBhpj0qIyZ2WZUnC8qJwOIXZSjxWRX1LOnBX7cbgU04ecXIdd4bXTLSqY0T3922WzOfBmBhAPzDHXASzAXKXUtyKSA7woIjagBNNOLyJdgDeVUjOUUg4RuQP4EbACbymlKkL9PQnMFZHrgYPAJU35xTRtgCXPQFAkjL7J15JoGkMjPIFCzRnA3R+v55ftmdgswq97jwJw+azfqpwbHxnEiO4dvL52Zl4J32w8xMq9R8kvcXBqv+jKjWTv3zCGmPDWk2msqfDGC2gjcIqH8mXASA/lGcAMt+Pvge89nHcUOL2B8mraC0e2wPZvYfKDhhLQtD4a4QkUFWpndM+OZOeXsjEtB4dLERxgpbjcWeW8/3fhEJISOniMEeSOUorP16bz+bo0ft1zFJeCntGhBAVYeWreiXDVgbb2GVdK7wTW+CdLngF7GIy5xdeSaE6GBnoCBVgtzL15XJUyp0ux/2ghm9NzefKH7cRHBnHVGO/y6z45bzuvL95Lj04h3DGlD+cldaGPGbfo4NEi5vy6n/AgG3ER7W/0D1oBaPyRrJ2w5Qtj129I+7PLtika4QlUHatF6B0TRu+YMB7/bhu9Y7xbD3pz6V5eX7yXP4ztzr/OH1JjttC9Uwh/+92gRsnUVtAKQON/LH0WbEEw7g5fS6I5WU4iJlB18kvKycovpVdMGOVOF1n5pRSUOigtd1HqcFLqMN/LXezNLuTpH3dw9pDOPHZezc5fY6AVgMa/OLYXNn1imH5CayYf17Qy3D2BTlIBHDhaBMCLC3by73n1LyyP69WJ5y9LwtqKN2o1N1oBaPyLpc+BxQYT/uRrSTRNQSNjAnmie6cQLhnZjQCbhdjwQGLDg4gMDiDQZiEwwEKgzYrdZjGObRZ6dArVnX89aAWg8R9yDsKGD2HkHyG8s6+l0TQFjYwJ5ImIoACevmR4EwilqaDt7nHWtD6WvQCIsfiraTvEDDTWADR+h1YAGv8gLwPW/Q+SroTIbr6WRtOUnGR2ME3zoRWAxj9Y/hK4nDDxHl9Lomlq3D2BNH6FVgAa31OQCWvehmGXQceevpZG09Q0MjuYpvnRCkDje1a8DM4yI9m7pu3RhJ5AmqZFKwCNbyk6Bqtnw+CLILqPr6XRNAdN6AmkaVq0AtD4lt/+C+WFMOk+X0uiaU60J5BfohWAxncU58DK12HgeRA70NfSaJoT7Qnkl+iNYBrfsf59KM2D1FXwysmFCdD4ORX2/zVva08vP0IrAI3v6DYKBl1AA9NEa1oj9lBIXwPBUb6WROOGVgAa35Ew2nhpNBqf4E1O4CARWSUiG0Rki4g8ZpZ/LCLrzdd+EVnvoW1/t3PWi0ieiNxt1j0qIuludTOqt9doNBpN8+HNDKAUmKqUKhCRAGCZiPyglLqs4gQReRbIrd5QKbUDSDLPsQLpwBdupzyvlHrmJOTXaDQaTSPxJiewAgrMwwDzVWm0FSPTwqXA1HoudTqwRyl1oHGiajQajaYp8coNVESspoknE5ivlFrpVn0qcEQptauey1wOfFit7A4R2Sgib4mIx9UhEblJRFJEJCUrK8sbcTUajUbjBV4pAKWUUymVBHQDRovIELfqK6jZsVdBROzAecAnbsWvAr0xTESHgGdrufcspVSyUio5JibGG3E1Go1G4wUN2gimlMoBFgHTAUTEBlwEfFxP07OBtUqpI27XOmIqFhfwBqDdQTQajaYF8cYLKEZEOpifg4EzgIqoTmcA25VSafVcpsYsQUTi3Q4vBDZ7KbNGo9FomgBvvIDigTmmF48FmKuU+tasq2HXF5EuwJtKqRnmcQgwDbi52nWfEpEkjAXl/R7qNRqNRtOMiOHk0zoQkSygtXsRRQPZvhbCj9DPoyb6mVRFP4+aNPSZ9FBK1VhEbVUKoC0gIilKqWRfy+Ev6OdRE/1MqqKfR02a6pnoaKAajUbTTtEKQKPRaNopWgG0PLN8LYCfoZ9HTfQzqYp+HjVpkmei1wA0Go2mnaJnABqNRtNO0QpAo9Fo2ik6IUwLICIfA/3Nww5AjlIqyYyR9DqQDLiAu5RSi3wiZAtTxzMJAN4ERmD8Pt9VSj3hGylbjjqex1XA/W6nDgNGKKXWt6yELU9tz8SsG4bxtxOB8bczSilV4gMxW4w6fiOJwDZgh1n3m1LqFm+uqRVAC1BH7oQbzfqhIhIL/CAio8z4SG2aOp7JJUCg+UxCgK0i8qFSar8PxGwxanseSqn3gffN8qHAV+2h84fan4kZg+w9YKZSaoOIdALKfSNly1FPDpY9FcqxIWgF0IJ4yJ0wCFgAoJTKFJEcjNnAKp8I6AM8PBMFhJp/5MFAGZDnI/FanHrya9Qbebct4uGZnAlsVEptAFBKHfWVbL6gATlY6kWvAbQs1XMnbADOFxGbiPQERgIJPpPON1R/Jp8ChRghwg8CzyiljvlKOB9QV36Ny2iHCoCaz6QfoETkRxFZKyIP+FA2X+DpN9JTRNaJyGIROdXbC+kZQBMhIj8DnT1U/VUp9ZX5ufoI7i1gIJCCEeNoBeBoTjlbkkY+k9GAE+gCRAFLReRnpdTeZhW2BWjk86hoOwYoUkq1qai5jXwmNmAiMAooAhaIyBql1IJmFbYFaOTzOAR0V0odFZGRwJciMlgpVe/MWSuAJkIpdUZd9W65E0a6tXEA97idswKoL7Naq6ExzwS4EpinlCoHMkVkOYZZrNUrgEY+jwo8ZdRr9TTymaQBi5VS2eY532M4DbR6BdDIfqQUI3c7Sqk1IrIHY5aUUt/9tAmo5aiRO0FEQkQk1Pw8DXAopbb6SkAf4CmfxEFgqhiEAmM5kX+ireMxv4aIWDAWxz/yiVS+xdMz+REYZv792IDJQHv5u/HUj8SY4foRkV5AX7wcMOkZQMvhaQQXC/woIi4gHZjZ4lL5Fk/P5BXgbYwEQQK8rZTa2NKC+YjaRvmTgLS2YAZrBDWeiVLquIg8B6zGcBr4Xin1nS+E8wGefiOTgH+KiAPDfHqLt+tmOhSERqPRtFO0CUij0WjaKVoBaDQaTTtFKwCNRqNpp2gFoNFoNO0UrQA0Go2mnaIVgEaj0bRTtALQaDSadsr/B/Zptor7sCVUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K6uxavWyrKfBEf6nR5pCNoh8TiNdTghAi2v+3BTgXOIRmk5jtbzYPyGlnmOs4Re0kpdwrpUyUUmZLKbOBYmCilLKsKzcRogfY9rIWUT3rFyBE98YSAoYuhIOfajEZfcz69ev59NNPyc7O7tCIfSpWq5Vzzz2XI0eOsGPHDvILnsFgiCEt7dpW25uM8cTFzebYsZe/U95PO4rqABidFrgrdWq0hZI6R6teZSH6nkCin1KA14QQOjTB8q6U8lMhRB3wLyGEHnACdwAIIVKBF6WUi/2vw9A8m37YA/MP0QV8TW5qlxzBXWIjYnY61pmpiDOFgeqBYedDRFJwLjrmKti/FI5+qY3bRzidTlavXs2IESO46qqr2s3f1BpTp07l0KFDrFv3X8aM/ZqBA+47zYh9JkMG/47NWxaTm/s4I0b8pbvT75d8uqeEzXk1KAJMBh1LdxwnKdLEuPTogMdIi7Zgc/uod3iIDjP23GRDdIkOBYWUcg/QIhxXSrkezeX1zOMlwOJTXtuBdl1K/LuKEL2Aq6iBmrcO4rN5MaaEU/9pHvXL8hF6hbDxCcRcNgRKdmlqInsNSNn9HQXA4HMhOhNW/wWGLNRSe/QSUkpKSkpQFIVVq1bh8/mYOnVqp4UEaPEUl1xyCStXvomUZtLT21fLhYcPIiP9ZoqOvUxGxvexWod19Tb6JfuO1/OT/+4kwqRHUQQur4+kSDMv3TzlRHLAQEiM1KrhVTa6QoKiH9L/8imE6DEaVh+jYWUhuigTiXeNw5ASjn1nBZ5SG03rjmPbXKYJivdu0TpEpARHSADojTDnt/DhnVC0AbLPDs64HbB//36WLFly2rG4uDiysrK6PKbLVUhsXD6qbwEGQ2SH7bOz76L4+JvkFzzF6FFPtty9fYv5cOdxTHqFb347j0hz12tehxk0e4bD893yEPu2EBIU3xGkKmlYXoAw60j6yXiUMO1LHT5RUy2pDi/2beV4Ku0Y9BakMQJx64rgTmLYYkBA4cZeERRr167lq6++OvE6JiaGefPmMWbMmG6Nu3vPowihY+LEXwXU3mCIJjvrLg7tfQO16V3GTb+mW9fvT+RX2RgQH94tIQEn80I154kK0b8ICYrvCEIRoIB1RuoJIXEqYRMScR6sofzx7cSGp2P25SOdHpQAy04EhCUaEkdA0cYgDno6LpeLpqYmli9fTk5ODmPHjuXcc8/FarV2SdV0JpWVh1CULfh8s4iPHxBwv+zsu/j6P0nk1cWRnNxAUnbHO5FvAwXVNoYkRnR7HLdPqyxo0ofSz/VHQu/KdwRfkxtUcB5qPaOpeVA0iXdprqIN9ktRsON5/+HgTyRjGhRv1eI0QMso+82TQUlD7vP5eOmll/j3v/9NTk4OGRkZXHzxxURGRgZFSADs2vUoIBg/7ped6ieEgqpq6pWPn9hJWV59UObT13hVicnQ/f+t26sJCmMAgY8hep/QjuI7grdaS7jmKW07OlofZyHhznHAOOTL94C7B5K0DZwN21+B3K9gyHnw5YOw8SmwVcCCPwU8jJSS/Px/UVu3hejoyezdU8nBgx5cLiuJiYlYLBauvfbaE1lfg0FtbSGI9Xg9k0lOHtnp/qoPotOqkZ50Pv7XLi78yVhSh8QEbX59gcWgwxGEetcnBEVoR9EvCQmK7wj6eC2fjnFA+yoPU1Yk0tEEeJDmHijfOewCsCbBludhwDmw/TXteG3gKchttjwOHvoN9fXbAair20xEJGRlD8BkvJPLL7+8RwzGO3Y+ihAqo0f/otN9VZ8Pjz2CmNEVzL58Ih/9cycf/XMX0clhJGZHMuKsFFIGRmkqwm8R8VZTUNKD25ur4nUiSC9E7xES398RhF89YB7WdirtZrxH9iCERKR0ftXcIXojTPieFk+x+21wN2opPioOBNRdVT3s2n0r9fXbCQsbzORJ7zNu7AuoqkJUpJ4rrriiR4REU1M5Pt8qXK4xZGS08ArvkPraCpA6rNFmwqNMXPbziYw8O5WIODO5Oyr44O87ePW331C0v/9l2G2PwYlWciuauh0o1+TSBEV4gOVTQ/QuoXflO4Lwb+mlR+2wre/IZgyAbvjkDtt2iQk3aHUuPrkHTJEw5QfwzRPgcYDB0mqXxsb9hIcPpr5+N07nMUaPevK0/EouVxqK0nNR39u3/x2dzsOgQfd1qX9dpZZ0ICJWM/xaIozMvl6LqXA7veTvrmLHikJWvLCPK38zmZjkYHoR9ByDEq3Y3D7KGpykRLX+3gWCzeXFYtCFvJ76KaEdRT/g8W2P86s1v+Jg9cEeu4ZQBOgEeDsWFFTkIjGiG9DNrLFtETsQxt+g/T14PqSOB6lC5aEWTaX0UVz8Flu2Xsyhww9SW7cJEMTGnn1GOyMCV49M1+msw+n6DIdjGIMGntOlMRqqNSeCqFbSmRvNeoZNS+bCn4xDZ1D4/Nm9uByBJ9TrSwYnWAE4WtHUQcv2aXL5OhWgF6J3Cb0z/YCvir6iqLGIZQXLmJk2k9vH3M6kpM6rNzpCGJSAdhSgItEFzVOoVS58Qqu/PXTRyeJI5Qda1OQ+cuSPFB9/A4DSUi1wLiJiFAbDmXmEzCDqemSq27b/A73eRUbGj7s8RkNNAxBFdGLbKVEiYs0sumM0H/1zF+/+eQsjZqYy+pw0zOHdi1HoSQYlajufoxVNzBrS9ezONpe3U0kEQ/QuoR1FP0ARCmennc1PJ/yUg9UH+f7y73PTsptYV7wuqNcRBgUZyI5Cp9NW+D2J3ghTb4foDIgdAHpLCzuFw1HM8ZK3SUm5ijmzD5yo+ZCY0DJXlBAmhHAHfZpuj52mpg+w27MYMXxxxx3aoKnGAcJHVFz7CZJTh8Rw9tVDaKhysvmjPD5/Zk+Xr9kbJFhNRJr15FZ2b0dhc3mDsqOw2+0cPHgQtzv4n4XvMiFB0U8IN4Rz+9jbWX7Fcn479beU2cq4a9VdLC9Y3mr74sZiviz8ErUTD3Sh73hH4frfY5hK3wLRi5tNRQcJw6D89LoNBYXPAgoDB9yDTmdi1MjHmTxpCVlZLfNLCswoPSAodu54CoPBTmbGD7tlJG9+m0oLDnfYdvTsNBb8YBTj5mdQmltP5bFGfD61X2ZWFUIQG26koZuqsqYgCApVVXnllVd45513WL9+fbfGCnE6IdVTP6DOVUeUUVOlWPQWrh9xPVcMvYJzl5zLb9b+hv1V+7lhxA0kh2ur0dXHVvObdb/B5rGRGZGJUWfE5XOxeMBiLhl0CRmRrRfiEToF6WtfUBgOPoEQPlyj78cc1LvsgMSRkHuyvrTTWUJp6fukpl6N2ZwCgBA6oqImttpdKBZEkI3ZXq+L2rq38XpSGD37ym6NddbFcynavY2Vr+zmxgcHYmjDaA/aw3fI5CSSsiPZveoY7/1tGwJByuAossfGM+KsFIzm/vPV1esUfGr3hJjN7SUxonufuOLi4hNFpTZv3sxZZ52F2dyrn+L/s4R2FH2MV/VS56ojznK6kdOkM/H6+a+zIGsBrx94nYXvL+Tur+7md+t/x91f3U1mRCY/nfBTsiKzSI9IJ9Wayn/2/IfFHyxu2ygewILYq2TjsYzFfOVPgnB3nSBpJDSVg01zD9V2E5Cd9aOAuhsM4SiKitcbvCDBPXtexGBoICnploBKpbZHdHwK0y4Px16VzKr/vhdQH0uEEb1BQfVK4tLCKT5Uy/p3c3jh3rV88PgOjm6v6NacgoVeEXg6WIB0hK2bxuyysjJefvllAK666ipcLhfbtm3r1pxCnKT/LEu+o9S56gCINbeMbxgQNYBHZz/KPU338N6R91ias5QaZw2XDb6M+6fdj1l/+mpp9bHV3P3V3RxvOs6IuBEtLyaADhZ+0hCO8NZ1OO/j7z2ELNxE7I3PEZbU9UysJ0j2J+or3YUzfQQlJUtITb0Kszk1oO4WczQuNzQ0VBAbm9nt6aiqj/KK1/D54ph9zs3dHg9gwux55O16i9xNKRwatYHhk89qt73BpOOmv5yF3qjDYNRhq3ex9n9HKM2tpySnjrLceqKTLMSndz/XUlfx+FQKq+1MH9huJYEOaeqGMdtms7F0qVZsc9GiRYwaNYodO3awceNGpk2bhsHQf50Bvi2EdhR9TLVDW0GfuaM4lTRrGvdMvIcvr/ySFVes4OGZD7cQEgDDYjS//HpXG3mEhNDqS7SHTg9qx7r+yN3Pkd64gapnLg2O7rzZ26lkB3n5/wIgO+vOgLuHWzX1VE1N4BHe7bH/wJsYjdXExtwQ1DQgF/zgYkyRNax7uxSft2NVmcVqxGDUHqDhUSbO/+EYbn30bL7/15moquT44bqgza0r7C9pwOHxMSW740DO9rC5vF0Otvvkk0+orq7me9/7HtOnTwfg7LPPxmazsWvXrm7NK4RGSFD0MdVOTVC0tqM4E4POQKq17RV2tDkaOLlLaY2Onulq5HD03kKkvW0vlqqdXxKhs+NQTWSKPEo++1f7gwaCOQrih9JQtpbS0vfJyPh+wLsJgOgoLZNrXX1+t6cipaS4+HmczigmTw5uYUZzeASj51pw22I4ntexYbvNcazaKtkThDxL3WFbgRYfMiW76zmrVFVid3dN9eTz+cjNzWX8+PEMGjToxPHs7GzS09P55ptv8PlCNS66S4fvjBDCDKwFTP7270kpHxRCjAeeA8yAF7hLSrnljL7DgHdOOTQQeEBK+YQQ4jHgIsAN5AK3SCnrun1H3zJO7CjM3du6g2YIN+vMbQoKIUSHqiclawyiXMWddwTj6NYNx/Yv/oJPCsQP11D73AKitjyKeu6tKOZups5Om0QeKzEYYhiQ3bmYhdjYQeTlQ1NTUffmAOTkfIzRWIZefxtGY/CNoYkZmUA1NeXFZA4d3aUxdHoFvUHB2dS3Nci3FtSQFRd2okJdV7D58zxZOykoysrKeO655wBISUk57ZwQglmzZvH222+zb98+xo0bd9p5l9NJY2U5qs+HUBSEEOgNRgxmM6rqw+N0nvg5vPk44bHZJA/wf0eFVt9FSu23qkqklEgVVJ/2t+qT/jbNf2teWdpvebKf/2fo1GQSMvtOhdgRgbwzLmCelLJJCGEA1gshlgEPAw9JKZcJIRYDjwJzTu0opTwMjAfw19w+DnzgP70S+K2U0iuE+BvwW+DX3b+lbxc1Tm1FFmvp3ta9mWhzNLXO2tZPCjrcUigJmseUmr8bWhEU9tJcUm07KIuYTFraCCpn/I6MLb+m8u37SLjlpS7N2dNYSGPRMqqUHVRHwKCEK9DrO/eliYhIR0pwOku7NIdTOZr7HIqwcNaMu7s9VmtExEQC1djqu/eQj04Oo7adbMA9jZSSbQW1zBnWveSRNpe24u/sjqLZWD1q1KhWi1ENGTKExMREVq5cSU1NDS6X60S9kpycHAw1FZjLA1tY6C3noDcHN6WNogiEIlB9KlXFTVxyb4uK0/2GQGpmS6BZD2Hw/0j/T/MSMgoo6WCo+UCulLLQP+4Xp5zbBHTP//BbSo2zBoNiIMIQvNWEbGvbEIDXk378Ofg+i0XsfxcuuqXF+bJXf0i2UAm/4EEA0s//IYUbnyE5/yO8tr+jDw9cBeGpz2PbhoXYTZrHjAiXJDhjSc+8NeAxmlEUIz5fOB539zyBioo2YzIdAS7BYumZFZ4mKMDRTUERmxpOyZG6IMyoa+RV2ai2ubuldoJTEgJ2wpjd1NTE9u1a9uCrrrqq1TaKojB37lzeffdd1qxZg9FoxGQyYTKZALBERnLRjff7dwMqXrcbj8uFolMwmMwYzBYMJhPv/ekP6I1OrvrtZG2dJUEoWlocIQRCOfnQb/03J//WCRQhTssSvPGDXHauLMJp8/TbKPyARLh/N7AdGAw8LaXcLIS4F1ghhPg7mq2jfRcOuBZ4u41zt3K6iurUa98B3AGQmdl9b5b+xvs57+NRPUHJeOryuaiwV5yIt2hBAKonYTDiTVyAsfw9pKoiTknjkb/8ZbKdW6mMm0XSyFn+IQXGub/AtPZuij9+jPTrHgl4vhUH/oXdpJLNeKLjZxE18Gr07dhgOkLKaFTZveyr+w88gU6nY+qUn3VrnPbI2btb+6ObQY0xSeEc2VyOx+07YfDuTU7YJwZ0bzfcXM8irBPG7KVLlyKlZMGCBe22GzFiBPfffz+Kopxwcc47eIDX33mXwSNHM2RqR48tUPRhWMJ9JGb1TFXCgeMT2LGikIK9VQyfntJxhz4gIGO2lNInpRwPpANThRCjgTuB+6SUGcB9QJt6ByGEEbgYWNLKud+h2TjeauPaz0spJ0spJyckdD2XTH+lTQ+lLrCxZCOqVJmY2LptIRDVk/R60Vd+hRTm04REU0UJEWsfwEkYCbe+elqf5Dk3YveZIG91p+ZbWbsWs0swcM4S4sbe2y0hAaDTxaMo9V32wqqoOIpevw2YQXR0erfm0h57tq8FIGXokG6N4/OpIDR7RV+wtaCW2HAjA+O7l+m2OWNsZ4L2SktLGTduHGed1fGD3mAwnBYHk3dISxUzYOiwgK6lN4bhaOq5ioSJWRFYY0zk7azssWt0l059wvzG5tXAIuBmYKn/1BJgajtdzwd2SCnLTz0ohLgZuBC4QfbH/AQ9jJQSi97CvIx5QRnvgw3vEEUEUxKntN5AiI69nurr0MkKPPGnr9RKXrqNeGMjvkV/R7HGnz6solAXNoQY51FkgB4mXnsFNcZ6EvVDTxNI3cFoTMZotGG327vUf+fOf6IoKuPG/jwo82kLQ5imF6841rV5NlNfbscaY0Lpo9Tc2wpqmJwV0+3dcJh/N9SsggoEg8HQ5QVByfHjICWDRwZWb8Uam4KrqaLHUqgIRTBgXAJFB2rwuPqnh1aH31AhRIIQItr/twU4FziEZpOY7W82D8hpZ5jrOEPtJIRYhGa8vlhK2b1vzLeU3LpcHF4HszNmd9y4A+oaalhn38TsmknYPi/SamTDaR9uoR1odxzh0gzhauLJ7LWO8nwGOzdQHjmViBk3tNpPDphNuM5F3b6vAppvU9HnSEUQkzA3oPaBYLGkotd7qK3tyFzWkoaGKiRf4/WOIilpbNDm1BqW2EOguCnJrevWOBVFjSRmdk0d4nQ62bZtGx999BGvvvoqH3/8cacEbEWjk4Jqe7fjJwBSoy0YdQo5FY1ttvH5fFRXV9PU1ITH46GhoYHY2K5du7q2Fr30YQkLbCcUl56FVBuoPt5zRaUGjo/H51EpOtA/C1cFohRMAV7z2ykU4F0p5adCiDrgX0IIPeDEb0cQQqQCL0opF/tfhwHnAWc6pD+F5nK70r8i2SSlDCxfw/8RNpRsAGBGyoxuj/XJxvfxKF4WGeZg21iKbUsZpoFRuHLrsZ6dRvTiAQEZs0VEvFbbofJk1lJXwXYsAjxDL26zX9i4C+DQ09j3ryRm3HkdXqepWvOktqbN73hSAWINz6CxEerq8klP75xaZ/v2pzEYXAwc0PVU4oHQ1FSOJawcJbyChtKuq1KdNg/1FQ6Gz+i8Ttvj8fDyyy9TUVGBxWIhNjaWXbt2cfjwYRYtWsSIESNwOByEh4e3mWp+W4G2oOiufQK0OtlDkqwcKGlo9bzD4eCll16iqqrqtOPR0dFdup7N7cFqNgXcPnXIIHI2QcGeHOLT4zvu0AVSh0RjCteTt6uSQRN6oARxNwnE62kP0MJvS0q5HmhRNEFKWQIsPuW1HWgRJCClHNzZyf5fY3PZZrIjs0mxdt+A9UnJ52Sracy86zJ8lQ5sW8txHKgGVdK0rpiwsfGBGbMjYnCHjUWp2nXimKeqAAB9fHab/aKHTsel6pHHd7XZRnXWU7HrT1gTZ1Jevxa9XmKKGx/wPXZEZFQ2pWXQ2MlYCo/Hjd3xMTolhezs9o2j3eXIkU8RApQIL54SIw67C0tY4A+tZnat1O4xc2TnH9RfffUVFRUVXH311YwYMQIhBGVlZXz44Ye8//77J9plZWVxww03YDQaW4yxJb8Gs0FhVGpwDLyjUiNZdVBT75ypysrNzaWqqorZs2djsVgoKCggNTWVsWM7v/Pzej14FT0xkWfWM2mb7HHDWPMGlObkAt1f1LWGolMYMDae/N1V+HwqOl3/ioUO5XrqI1SpsrNiJwuyuv9gOlpwiIPKUX4cexuKoqAkhRN94UCiLxyIr8lNxVO7qHr9AEqYHkXR3O98Li+Hn9uLrHcx4v6pKKcYRNWIoRgd72tqKiGQtVpaDEPy0DbnIHQ66pQkLI1HW79fVyN7l0+hKtIHBUvBDHGOiKDZJwDCwzUDtNfbOaPgrt1vYjbXERfbvVTigVBa9il6vZGh40ZxoMRGzu4ixs7o3O6nJKeWXV8eY+CEhE554thsNg4fPszGjRuZNGkSI0/R0ScnJ3PHHXeQk5NDQUEBbreb7du3s2LFCi666KIWY31ztIop2bEYgvRAG5kSybvbiimpd5IWfXpm3ZKSEhRFYdasWej1+hNpOrpCTXk5CIE1MnDX57j0FIRiovpY++lhfB4vSx55hrrSfCIT0zn7mkvJHDWo3T6nMnB8Aoc2llFyuI6MLiwAepL+Jba+Q3xd9DWN7kamJLdheO4E7299B0UKLp3e0p9cZzUSd/MopNOHt8yOt9xGQ2EDOX/cTGRpE1F2D0UrCk60l6oPXe1uJGZtBwLIpgo8qkJYfPteSc64scSKajy1pwe92cu+YePqyZqQAFJdqQwKW8jIs97t5p2fjsWsuQWrvpqA+0gpKS97A4/HyujRwUn+19Z11q37OWbzHnTKbEZN1oRD3v7AAwSdNg+bP8njk6f2YIkwMHlxdsB9S0pK+Oc//8nHH39MXFxcq26liqIwbNgwFi5cyEUXXcTMmTPZvn07+/efXiekrN5JTkUTs4YETw0za6imhvtgR/Fpx2tqatiyZQuDBw8OSs6tmiptERHZiR2FEAKzNZnG6uJ22739h39y/MAX2BtKKD28hvf++CuKDxcEfJ2MEbHoTTpyd/U/76eQoOgiywuWM+a1Mfxhwx84Wtv6KrotvKqXZ3Y/Q3ZkNguzF7bbtqKmhId+fRWv/fcvrZ6vqihnaeOnzBSTSU5Ka7WNMSWcpHsmaOkE7F6qn9mNyePDMSkJG+BdfxyfU/M4UcuPY/Dsx5N0yirSXoPDZ8DcgfHPMHIRQkDtptMFQP62e3ArHkZbr2X+vFxGnL+O7OnPYIwJzD0xUPR6TYWjysA9R44e/RpLWBHW8ItP9D+Vw4c/Z9u257o1L6/Xxdq1t+H2fIjDPpnZc/5FYlosUu+h8ljgleG+ev0g2z4rQKcXXPbziSRkBLYqllKyYsUKDAYD1113HbfffvuJoLP2mDdvHmlpaXzyySfU1590D12Xoz3Izh4cPHf1QQlWZg2J541NhaelLM/NzcXr9bJo0aKgXKfOb+eIiu5ckGBkQjpuR4XmktwKhzcdpPzoWhIGTOe+t/7Hlf/vn0jgg78+gtsZWC13vVFH1shYjm4rx2X39KtCVSFB0QU8qocHv9Eik9/PeZ/LPr6MYw3HAu7/4t4XOVJ7hLsn3I1eaX+VVFh8GGuBg6qPvmHZitdbnP/zij/gVNzcM+cX7Y5TV1POmvIPOVC3AadOYL15FEOuGopuVhpmCQVLNWGnJKYjpe60YDDhqsOFuUM1Ufz0y/GoCu4jJz2f7EUrKbfUkKYfR9LUP7fbv7s0G15lJ/zxc44+g89nYMKEn7Z6vvj43dQ3PNblOfl8Hr5YeSle3xqaGueycOGbGPwCyRwDzprA5mqrd5G/p4qMETFc/otJRMa3XvjI5/Oxf/9+VqxYwbJly1i7di0ffvghhYWFzJs3j2HDhgVczEen03H55ZejqipLly5FVbWH5MbcauKtRoYnBzdy/ZaZ2ZQ3uPh878ldVllZGSaTiZiY7kV/N9NQpxnho+I6l1stPjMbpIvSI63vKtb+9y0QOi752Z0IIcgaM4ipl/wAt72EJX96MuDrDJyQgMvu5cWfreO5H6/mf3/cgi+Q8sU9TEhQdIED1Qewe+08cvYjJ3YEx5oCExT7q/bz3O7nWDxgMQsCMJyWlhec+Hvvq+/w7HO/we7SVqEfbVjCl+p6bo28jmFZbfuE532xkf8+8HPKHUc41LCHoX+YTsJI7Ysy6PwBNOgE6p5KvC4fuJoQwgf+TLQAOncjHiWsw7kawyOpF3Hoq7WsqNLnJXf//QgVsiY+2mH/7tJsX2gzhckZVFUdxWDYhWAm4eEtV8dud/fzKO3Z8xIm0xEE13HRRc+fVhshLj0M4TFTmNOxO+/uVcdAwswrhxCb0nJnJ6Xk4MGDPP300yxZsoStW7eya9cuvvrqKw4cOMDo0aOZNKmF70mHxMXFsXjxYgoLC1m9ejUARyoaGZkaFfT4jTlDExmUEM6zq3NR/cK+uLiYtLS0oNmOGhs0z6q4xKRO9UsfrqkKC/cdanHu4Dd7aCjfTdqI2UQlnhRAs647n8SBsyjLWcOG975o0a81Bk1KZM4Nwzjr8sGkDYum+nhTv4itCBmzu8CKghWYdCbmZMxhdsZs1havZVn+Ms5KbT9K1O1z89v1vyXeEs/90+4P6FpFR7Uo0gse+SPvvPAIuq/38cSGa9CnJrN08B5GMpi7LmiZbqKxqIK1L7yMT3rJPboVqzkGZASKzoTulFQJQhGEz0xDt7aYos/yyV6QggCEOBn8ZPDW4zRnBzRfT/QQYmq20FCwjH2HforDpDJQmYopqntRyD3B7t1PIoRk1Kh7Wj2fl3dyZySlihCdW1e5PXbKyv+DqsaxcMGDLVxNpy0axtJdu/n0mV3Mv9nN8PHZrY7j86qaoADi0qwtznu9Xj744AP2799PQkIC11xzDcOGDUNRFDweD3q9vlsP2vHjx1NQUMDatWuJSUhi3/EGhiS2nEd3URTB3fOGcO87u/jiQDme/K2Ul5dz3nkdu1sHSnOsSHQndxTZ4zRHjk3vPcnxwwex1dZhjY2hoaqcutJDCF0Yi3/cMjfaVb+/h+fvymXTe/8he9wwUoe0X+RLp1MYNUtTIUskxw7Wouj6JqDyVEKCogvUOGuIt8QTYdS23hcMvIBPcj8h2hRNnDmOOEscseZYLHoL+6v3o0qVnNoc3D43+fX5PDHnCaJMgRnTaouLEWGS4YMm8OBfl7B89X/Z8ME7ROWXMzIsjgduegyD6XT3xdqcYt774/+jwaXpY1NiB3PJww/w+u//jNfV0lc9Y0EWR9cWI3eUI85PREqB9GqrmKbCPUTq7NQHGoSWPAY7W9mZ9xMwwiDdDLJmtVSZ9TUuVxMe7ypUdTgpKa3fW1nZ2hN/FxV9Q1bWrE5dY+OGhzAaG4iN+RN6fctkb6lZiYy7KIFdn1Tw5YuHGP5Udqvj6PQK4+ZnsHNlEXUVdqITT+7ubDYb77//Pnl5ecyfP5+zzjrrtHQVwaru1ryr+Oj9JYQxlnpH5116A+HCsSn8a1UOz6/czdj67YwZM6ZbXk5n4vX5QFXRd/L/Ygo/eb/H9q4EoMavhYpIGMvcm28gMj66RT9zmJlLf/lblvzxF7z/yCP88LknMQZgHwItZTnQL1xlQ4KiC2RGZLIsfxn1rnqiTFHcPuZ2tpVt49X9r3bYNzU8lXPSzwn4WrK8AXHK6m3RnOuZN/lS/n3b1dxguIDs7NPDUSr35vL+3x7A5XNw0fd/rflnz5uGTq9HoIBsqe9U9Aq+YbFEHK6h5tPVxAmJSNPy99esfhkrEDHt2oDmqx8wgZ1JmhBM1c2jKfseqpoaSIiMDvieu4oQAlUVKErHqSB27PgPBoOTlOS2M9Xa7btRdGYMBid5eZ+dEBT19SVs3fYwNtsuQJKZ8WMmTLjptL6bNv8bt2cpHs8oxo9v+383a/F49nz9McLXMlbhVMbNz2DP18V8/K9dXPbziVhjTOzdu5dly5bhcrm45JJLmDCh59JUG41G5s6dy9KlS5lqrcEU27EqsivodQo/njuYzz9cQq2wMGfOnG7XKz8VrYhR543EBqOJsKhoHA31/PDZd3C7HHz496eYsHAh489rX5BljhrAlIt/wNaPnuadP/yDG/786zYDGU+l2dYW2lF8S4kzx6FKldy6XCYmTSTVmspHl36EV/XS6G6k0d1InauOOlcdadY0wgxhJFoSyW/IJ8GSgEEX2GqmrqkGSyOYx53uzWS0hpEYmcWR/RuZUX4jYUmaoa9kyz4+eOKPqKhc8bM/kD7l9JWyUBRkK4ICIOuigVQdqsa732/UTtYy9ZoKVlHviyB69JyA5hwxcg74a9qX+L6C3K8o9Joocc1i0ojvM2HQ9B6NVZBSh8/XvpeJqqrU1L6HEHEMH35Zq228Xjd6QyFSnYHbfRCjcQkrvvgAn8+IyWRHCDAYItDr7VRW/Yny8rNIStKE9vGSLdhsT+B2RzNn9ovt3q+qqvgceiIS2n8YhkeZuODHY/noiZ1s+noX+dV7KCkpIS0tjUsuuYTExJ6P5o3zq2uyvcWoZcVs2DeEs0YHHicQKJeOT+Xh9zJokBb+YAhu4ShVVQNJUNAq0ckp2Ovr0JshPCaOW/7+YMB9z7n+fI4d2E9Zzmpe/cVDzL35BrLGDG5XYKg+iRCclpK8r+j7Pc23DJvHxgt7X2Bw9GDGxJ8slqIIBaPOSJwljuyobMYnjmdOxhyGxAwhzZqGQWdgaMxQYsyBe2/sOvANCoKMQSNanDvnlttweBpZ+vsHOLpsPZ/85k+88/j9CEXh6t890kJIACg6HaraumEsLN5Ck0mP0ePPNRORgOpxE+M7TkPU6BMxFR2hhJ18YJW45+Kx3k8js0g3r6G26EaWLD+XdXveaXMe3UVKPV6vo902hw59gtlcQXT0FW1+UQsL16LTeYmNnYbZPBMARVExmew4HDFYLD9i4YLtDBn8JDqdj8NHtHpcNlsV+/f9E4DRo14kPLz9B3hxQRk6r5nErI51/unDYrDHHOWbvcux2Wxccskl3Hbbbb0iJADS0tL4xS9+wTU33IwqFD745PMTnlDBRK9TSPOrcX751jdBHbum5Dh0cc6JA7SFQOHuHV3qf93DPyN91AJqj+9g6SM/41833cRr9z7Ajpc/wWNztmiv+lSUfqB2gtCOolPUu+r55ZpfUuWo4q+z/hrwzqCr5BzZCcDoES23tllnTWRewe189dELfPTqX1GEjqGDZzDrh7cQmdl6PQpFp0O294BODEMp1fzlRUQidfu+JlbxoWQHlraguKyGm59aydjsBYyIqOW2K5/361dvo6apmhVbXwd1Ce6q+3l3+XNkZv+es0Z2nDlXSkneFw9QXvEprsgmwptSGTz2fmLHtIxBUVULqtp2cjmAgoKX0OlNTJxwZ5ttSkrWAJA94Dxiom8j5+hchg29AI/HjdFoOrFLyMo6l0OHY3C73mTTJjf1Df9Dr7fjcY8nK6tjVdDh7ZqReuDYjtO4CCHwmWyE6aO5++6fBCUArbNYrVZGDLGSNHwy1Ye28O6qLVzbgeqlK0wfmsLBygK+LAxuqVeft+vjZY4ex67ln1C0bw9Dp5/d6f6KonDNAz+lLO8KdixbTdG+HVSV7mBtjWTzRgOJhhrGLRrE4IunaXP1SUQ/UDtBSFAEzCe5n3D/es1T6ffTf8/k5OCWRWyNytyjKAbJwMzWXV/HX38xyaOGUV9cRtrU0VgT2vfksERGU1dqw+v1ode3VHXoY0yIMk2/L0xm3PlbATAN7FhQfLl+J/d9fBQHZi5UFnH71RefpnKJtcZx3dz7cLh/zLKtb6B3/Yfyop9RlvoFydEtV8RSSur3rebYvheoFbvwJLowmI2EN6TQFFvMzpK7SNoymZE3vopispzSLxJk27UDysoPYDQdABZgbqfGd2PTDiCahHhtFTliuBaAaDKdrgpRFD1W68W43a9hs7+M251ISsqDjBl9RYf/M4Dc7dVIvcKQsRkBtTcbLbjcjj4REqdyx+Xn8cBf9rBj29agCgqvT8Xp9fG/LSfdzY+WVDM4tfs15QESswZSUtu12hJZY8YDUFGQ1605JA9MY/GPb0D1XsM/r7+ENFGONbyCPGc67k9yTwgK1SfRhQTFt4t/bP8HAH8++89cPKjtLKrBwmFrxHSgBke2FaUdY17yuGEkjwsswjk2NYnSw17KjpaSPrxlYR7p8gEepBQIvcBXcQhVQsTglkLxwMEc3lu7n6QII0cqbHxYasSK5OUrBnLOtJb1i5uxGI1cPvM29uSMpKLwRvZ8fC1J31vVQo+/+7+XUp2yDxLAXBlJUuMChlz6KIrBiKMqn32rb6N8wDbql57F6MlPEzVEc01WRBQobScF3LvnXwgFxo5tPcAOQFV96HR5qGpgxuFpU3/B9u0WzOZ4zpl1HYYA9eqbv9yHr85MynilVcHdGhZzGE3OuoDa9iQmo4GIlAG4jx/keGUdaQnR3R7T5fEx4Y8rsfsr3ikCVAlf7s4PmqAwGPRIRbSafLAjjGYzOoOBuvLu12UHcFRoeaeSByQw6w838fpt72I0nNzxqz7ZLwzZELJRBMyz5z4LgOiyKaxzfPrCEwDExgevNGLacM3wWLD7cKvnvZUOvGoGQkh8+btQ6oto8lkwRZ5MUGa32/ntE//jklf28XK+gb/skbxfFsYQk4PlvzqvXSHRjJSS0gdfJfJTHbr0Qo58fPpDu/bwKqpT9hFZnM600Z8w87qdDLvkCRSD5hlkiR/A5CtWkW27FldkI9uPfo9Db/8IKSU6XRx6Q1Or6Q/s9lpUuRaPezSJCcPbnF9x8Wb0ejfR0YHl4TIaw5gx45dMmHBLwELC7faw9eNjSKOLC24KPCOp1RqBT7hw2AJLC9GTzJ42AUXAp+u2B2W8T/eUnhASAKt/Pgs9PrbmVbXTq3MYDAYQCh5X1/5/1phYnI2tp0PvLE0lxwGwREUD4BJmTvWcVX1qnxWlOpOQoAiQoTFDiTRGsq18W1DHbcsYOPfGHwAg41pP1dAVBk4YBUBpzpFWz8tGNz60RG9qzg70jkociubqun1fDr956gPO+sMnvF0WwbBwD+/cNJq3bxrD6rsmsOyha0iJD8xQ/+Vj/yFzx1ryfdcRXpxAccTnbH1jLpXb3sfndnBky+8RLhi94BWsya2r3YQQDLroz0wZ9wGW+niOJ62kaNmjGI3J6HRebLaKFn127nwOvd7NwIG3tzu/Y8VaoF1W5rkB3U9X2PTFXoTbxNgFyZg7kWY8JSUZBOzf0fp72JvMHDMYNzqOHz/e5TFqbC6cHh8/+e8Ofr5k94njf7tiDJnxkaSavRysbGno7SrNcSV2e9ei7s3JcUgpefhHF/PHX17Nfz/7N2obnoQdYS8rAyAsLh5VVfHoLFgsJx/J0idDxuxvG4pQmJg0ka1lW7s9Vl7VDr7Ke4cNpVvY11BFisnM8+e/Q1LEwBNt4mKT8AlJff6xLm2TWyM8KhKdMYaakoIW5w7n1rBF/Ygr9csA0E09H922h9nvzeb8X79DjbACRgbobPxqdjzXLVzcpTl5fSr6996mKGkgVzzxIDjuY//Ht1KVuJs9Db+C1b+CNEiuPgdL8sAOx4tIH8OUK75m44fTyDO/QJjnSpoUqK4+itV6Mk2Dz+ejvn4pkMSQIee3O2ZDw3aktJKSMrrT9xco1RWannzM1LZTt7fG9DkTWL9tFVs372DyrI53bz2Joih49FY8jXWd7ru3uI7XNhTy3o6WuZNe/v4U5g3X7FYjEsysPObD5nARbul+kJ/BX1vD2dQEsZ1XZ9WkKLAHhM2DcPkofX0FD25Zx133/ZOU6NaTcrZF0zFNRRqWkIirtgkpdJitJ+NpfCHV07eTKUlTONZ4jHJbeceNT6HBUckn+5/kVysu5dz/jueSz27mXwc/52hTNZOikznmdPD9z66kvPGkkUwoCq6x8ZiO1LF0+fNBuwdrTBr2+pY61t//bw+LDe8SqRThGnE/W1e/QbTewQ79CBL0Hu4YovLFj8bz9V9u5PpF07osuHYvW01yQwWmK65Cr1PQW6MZd/1SZk5bS5btCqwVSQz03MzIK18OeEy9IYyxk59H6qFJWQJoVe5O5cCB9zCZa4iLv7pd33VNKB8FObhn4z38Gha9vnNfwfAIC8lR2ZQ3FlJVFng69Z7CaI1C527fy+xM3tlaxEVPfXNCSIQbdcSEGbh91gAevmTUCSEBMHVwIioKa/fltzVcp2hOiGhrCjxr76ksmHcdAJmLZvHr/3xAxMyRRB2y8fzPb2fZhnc6NdaB97Usy2GJidhLNLd0S9RJ1eW3ykYhhDALIbYIIXYLIfYLIR7yHx8vhNgkhNglhNgmhJjaSt9h/vPNPw1CiHv952KFECuFEDn+38FJD9mDNHs6Bap+cnoa+dEn53HOu3O5f9sLfFl+lCSzlduHzOHtBU+y+rpdPHvRSv467V7K3e4WwuKG638FQHVdSzVKV4lNy0T11tJQdfLL7Xb72NbQRAyNbE27iX1mKzNy/s6usBlcf+cfWPHnG7n/tosYmt25FVNrHP9sJR5Fx4TvnR7oZo5OY/BFjzLtxg0MWPhApx/SUQNmMDj87hOva7ct90fhahQdexWPx8KE8Xe0O055+W4MBjuRkT3r1SZ92v0pXfCwPm/xXEDlrVeW4PP2bcK4cGsEZjydiqd4Y6NWAOiuOYP48Mdnsf/hRex8YAG/u2AkN83IPq3t/HHarvKbg11Xb52KxaJFlNsaOyfcmhmQOgwVSV11BQajkTt++ihn3/cTDKrCvidf57F//Rinu/04nmYGT9RsYIXr1tBUVqfNL/ZkwkctjuJbIigAFzBPSjkOGA8sEkJMBx4FHpJSjgce8L8+DSnlYSnleH+bSYAd+MB/+jfAKinlEGCV/3W/ZljMMCIMEQGpn9xeJ3d+dhHf1JSxMHkI/5xxHxuu28hbl6/np2f9m9Epc0+sbBcM+0GrwqKuXjPiRUREB+0ekgYMACTH9p+soVFS0kiYcGASXpS6fMbs+D17jeMZde8HJKdnBu3aAGG7t3I8fShRMYEXjgmUzHPuYbT1r+iPmoleupVt8+ZTtmMHx4/vwGw+gtFwLkZj+6knCgu/BCAjo+P4ju7QVO1GVTyEWTsfeTxweDpjBk2j1lXKk397nvqarj30gkkgKSmaKa13YtIr/GrRcMZntL8+zE6OJVLnYffx4BiQw61aYKOtqWv/M5PRjNsssdfVnTg2ffr5/PiJ15DDElA2FPK3+65hy65VHY414S+PYvSpHNu/B0+9Nh9j1CmCQu0/NooOZyE1mvdpBv+P9P80O6JHAR3lSp4P5Eopm+sJXgK85v/7NeDSwKfdN+gUHROTJrK9vH0vD5/q5b7ll7Ctvpo7hszhbws/4Nyht2I2tJ2/vzVhUevfSURHBa+SWOpQbYVWln+yrGNVnZNYoX1QJ9nWUSNiGPTj91vEDHSXhvIq0mqO4xk7MajjnkrS1Ks4+9bdGM6/G1N9HSV3/JC9mx9HVRXGj7+3w/519VvxeMykp/fsjsJe50Uf5uuyeuvy7y1i3JAZ1LsreOOFJUGeXefobH0dj0/tVAnVwdE68hrEaTvErmKN1B5ZXVU9AfjC9HgaTu8fFRXHrx56lewbLsDQ4GXdX/7Jo/dfTU3RwTbHUXQ6ksIiKKqtomKP9kzRR52M7flWqZ4AhBA6IcQuoAJYKaXcDNwLPCaEOAb8HfhtB8NcC7x9yuskKWUpgP93q3kIhBB3+FVb2yor+75E4OSkyRQ0FFBpb30uqqry25VXsra6hOuzJnP3Wf8OeOwzhcWxCs2zJaaVgLSuEp2sCZ0VW46yfodmq6hudBPPySCkgmE/ICyIwqmZ3HXaTixmas8+hHV6PaN+8hMi//Y3zE1NxK3ais87gdjY7A77SvUwPu+goCaiO5XDuwsoOHIcX6ORiKSuR/YLIbjshoUMTBlFlb2IPZtbd3nuaRx2O24RuE/M9S9sosHpxeHxBVzBbUp2DDZpYE9u99VPEX5XVLut67VGZLgBYXO3eu6Ki+/kB0++iHtCLOTbePVXP2fdY3fjaqhttf05P7obBdh4eBXuxg8wRJ1M5fJtUz0hpfT51UfpwFQhxGjgTuA+KWUGcB/wUlv9hRBG4GKg00sfKeXzUsrJUsrJCQnBK73YVTqyU/xp9fdYVpbLxanD+fU5bf5L2uRUYfH5oU8BiI8OXiyFwax98PJUO7e+s4OC4w28s6HwxI4CYPSFdwXteqdSUay5A2YObT8nf7AYuHAhjgmS8DUKqSkXdNi+uvoIBmMjVmvPZGF956kv+fLZPD77x2EEChPmdOzV1RGXXX8+CgY+/Pw9dnyzv+MOQcbZVIfXEHhtig25mtHWp0oKqu0B9Zk/Vvu8rNxT2EHLjon0V8pz2AO7dmvozCaEu22bTGJMKrfPmcLS2cdR4urZsi2fl++6nt2v/QXVe3pm4+Szzua8S64GQPXmY3ef3Kl863YUzUgp64DVwCLgZmCp/9QSoIUx+xTOB3ZIKU91FyoXQqQA+H8Hz2LbgwyPHU64IZxtZS0FxT/W/Yglx/YwPyGTP85/p1N621NpFhZGr/Yh2fz6n1j1399xYPO7NNV1Pip0+Su/4KOnfqiN9ZmWsE7qrfiAy5/8hq9qG/i5XvPA2B82FWtE8O0HAML/5TRGBreEZlv4fD7Ij8CdJSmvXN9h+/wCrQpZWtqcoM6jrrqJN/66kqp9CqZ4N0q0jZghKqOmdD/zakRkOJdddBVSqnztr0DXm+jdTZisgX1eXJ7TVUfhxsB2bZOHpGEWXrYWdN/LyxodDap6ooBRV9CFmdG5298NRY+4nHi9k9en1HPeDWcTE6by5eff8OaPLqF4zfuntR3xve8zYtolAKfVllG/TXEUQogEwCOlrBNCWIBzgb+h2SRmowmOeUBOO8Ncx+lqJ4CP0YTNX/2/P+rs5PsCvaJnQuKEFjuK5zf/klfyvmF6TCJ/X/hBl4VEMwuG/QDPfBt765dTdaSOsj272cVu4HWMESqRyRZi01NIHjic9CHTSMqcgKLT3s5dq1+ivOAg+1cc1DLvq/4SoT+WFB88QJ0+kmGzkpniTOOFA8cZHWYhISwMmsA3uyMNYtdRXFrglLAEL4iwPY58+BFhdU0Uz09AVddhs1UTHq75ztts1ezd+wI1tRsBD0JYUdUy9HojWVntVyoMFFujgxVvbaVktwukQmS2l+t/fi56Q3DDl8ZMHsr2zcMpqNhPQ20TkTHBrz7XGvklVZjwEBUf2E6/eTcBYDXpSYwMzAamKAqDI+FIbfcz1SqKDkV2T1AYwyyoHtqNb9JHpfHgqB9y65GXqctK5ppnPyPnnb+yetk63nnmFc7ZupwpP3/+RFZmg79ei954Uh3Zn3YUgXxiU4DXhBA6tB3Iu1LKT4UQdcC/hBB6wAncASCESAVelFIu9r8OA84DfnjGuH8F3hVC3AYUAVcF4X56hclJk3ni+BNUOaqIt8Tz351/5KlDyxgfGcNTiz9Br2u/CE2gXHDOPVxwzj2oqkptxRGOHdlAee4Bqo4VU1/aSNXRfI58XQAsRzGoWBN0OBskJ3evp3/Iju75mJoCB8fNAxhuUfjVNeP5mXs0RqMep+NrjuQdZuyonjM0E6WtPBuLS0hOCU7unvao+t/bRBkMZF7zG4qr7mHLlsfIzl7IkSMvIpRt6HReIBIpzSjKMcxmOw7HaAyG4FRve+3Br5F2M/oYF+dcPYKRE4Jfu6GZUWOHUbBqP7s2H+KcRT2fsBJg7U7NUDt+xOAOWmp8sFOLm7h8Qhq/v7DtGu+tMTEjkn377BwuKmdYZufqXZ+JUQjsrq5He5vDrDiloMleT0R4dJvtLP4qllL1IHR6hl7//xhwYRXL//QT1m4txfbQ9zjn/pdQjCa8Hk0lpT9zR9FPUnh0KCiklHuAFkpbKeV6NJfXM4+XAItPeW0HWjwVpJTVaJ5Q3zqmJGv+z9vLt+O0HeJve95hWLiV5xZ/gkkf/MpfiqIQlzycuOThcEpxPJejnuKjGyk5uoOKglxqj1fhbjq5vU8eFc7oORdQtH8DOeuK+PiRFwCFvNiBzI3SVvVGf/1ssyWcoT0pJICBc2fiev5xDr/zIUOm9GxUcUV+PtEHDuI5eyZjx15I3vKnMRqXkJe/BKHo8fkmkp31fQYPPu/E7q+urhiLJXjhPKpLR1iqm1sfuDBoY7bFqIlD+OxLQUFeAefQO4IiNy8fn1SYOaZtQXGkrIGr/rOJ354/nAmZMXy8u5TyBicx4Z1bTM0bk8nr+w7xxa68bgsKa5iZmiY7UlURXdj5W6yROIGauop2BYXXX/5WeE7uXgyR8Vz4l7f46k+3s/1ABfk/uozZV12Dz6PNw2A6+UjuT8bsUAqPLjAibgQWvYVX9zzLodqjZJnNvHjBR4Sbont1HiZLFIPGLGLQmEUnjlWXH0JvMBEVO+DEsXHn3MSKsF+y7/ODkAQFlkwy44Pv1dQRAyeM4JMhE0hdsZSqe+8gPr17X/j2yH/9dSJ9PlJvuw2AaVOfZtu2RwgLy2bs2NuJjGx57ejolhl1u4NEoutk5HVXCQu3YBYRVNZ2LmtAd3DWloElFmM7qrQvD1VQ7/Dwm6V7+fEcbUc1LLnzNqqZo7IxsJ9NedXc3XHzdklOSaUqv5Di3BwyhgSWeflUwq2R1AK19RVkpbWdgiUr82zY+Sg7i9bgsFcxffxtREVnI3Q65j3wEpnv/YN1n6zgg1dP+vgYWqie+oeNon/M4luGQTEwzBrDvtpckkwGXrrgPaIsPffQ6wxxScNPExLNLLz5Ma5/9EHcc64BIRiZ3n2Pm64w/Pe/xuR1880Df+3R68gtW7DFxhA7Rdv9xcYOZMGCFzn77P/XqpD4v4DFHI7LHbwEeu3R5HARptqJTWzfI29g/MkAsqdX56JTBD9f0PmHs0GvY4DVx8Gq7hcyGjFuPAB7t2zuUn9rhLbrrG+obrddTPQARqo6XrTl8IvCD3n2i5MiTgjBkKt+zs3/eY+5s/0VLIUJk7l/2ihCgqKTlB6tY/V/DzNOKWSgycez814iwZrd19MKiJSsKRyqcBFnqSchqnfKZ57JkKnjyJ06n4Ebv+Do9n09dh1dfQPe+PgezdfUMRJk713foDfik8GtCNcWxyo0D6ToqNaLP3l9Km9tKuCut04vG/r7C0YQbuqaImNCmpUar5FjlXVd6t/M0FGjQUoKCrqWPyryhKDo2Avr4ZkPM1/R/ke59rIW53WWSCbe9RjTJ12EOeoWDKc4emiR2f1DUIRUTwEgVUnhvmp2rCikNLcevUnH4JE/YsKQf2NoEm2ECvZPDlWaGRwXWC6anmLGH39L8flrKPj7vxn89n965BqqXg+e3nlo9heio2KoaCzkWF4pGQODF3vTGuXVWoBmzBmC4uaXt2DSK3x1qAKvetKF9OfnDeVHswdiCLBAU2ucMzKNdw7ns2L7UX7QDYO9wWAgwmigpqkB1edrtzBYa0T5g1FtAWTNHTb0Yp4YejG/eHMWhzwtK+uVVhZhdzupUj3oFf1p/if9qWZ2/5hFPyZvZyX/+9MWPntmD021LmZdM5RbHzub8XO0coU1le15BfcvCiuLKLfFMCkz+Ab3zpCYmULx8IlEHem5HYVMTMBQGbyCN53leH4FwmfEZO29r9icBZpb76pl63r8WlV1WoBmXPRJe0NZvZM1Ryr54kD5aUIiyqLnx3MHdUtIAMwdNwg9PjYc7X6GhsyMdLxGC0WHD3S6b6x/N25vCjz/VJwhgkJFcqz60GnHb9y2m5lH7Pxw2tlsz7LiajqpOgypnr4FuB1evnzlAMv+sxeAc28ZyQ1/nM7YuekYjDoSU7UKaU0N3auf25t8vW8nALOHt13drbcwhIejBCF3T1voBg0mrKkJR03fpOJe9to2QDLrkp6raXEmqVmJxJhTKSrP6fGsslKL0EF3iteQx59B1qhXCDdpQkEA4UZ9t+OKAMLMJjIsXvZVdL+639hJk0EIdm/Z0um+zYLCYQs8X9TFo28G4KEVPzqZukRK9ptP1kr/eJqV7G2HSVm1k4yVO/nTAivrLX2bHbiZkKBohdKjdfzvT1s4sqWMKRdkc83vpjBsWjK6U7aBYREx+FyROJzdTyvQW2zIrSRM72DSoLF9PRV8egN66e24YRcJy9bSPtQc7v0cSJXl1TjLDMQMVEjL7l3D+cCBg1AVD8cLezbRgd6vrnF7Tr6HVr+rtapKJvizwkogPiI4cSkAY1PCqHDrqazveq4mgMHDRyCkSmFR57+/YaZwPDoVlz1wQTFq1DX8zjqSzZ5qPt2gOXIcKzn9s3ljXS3Xu01c4TFxvseI16hQlxncxJxdJWSjOAWfT2XbZwVsX1ZARJyZy385ieSBbacnkJ4UvPJYp65RsX0d3gonqeef193pdgq318uGwigmp9ee+JL3JT6dDoPagzuKyCgk4OtGltCusuPrHAQ6xs9t6X3W00T601Q31nbvQdoRcVGayqm67qTe3WLQPldeVbL+aBXhRh1CCP5w8aigXXfWsGQ+yjvOih1HuXHuuC6Po9PpiAmzUFffgNfjQW/oXIJGrxGkvXO2vqsueIEP/zuLpw//lwum/4p79hxAZ8rijSQXc4ZPa2ErGbZuL4awrieODCahHYUfj8vHB3/fwbbPCxg2I4Vr/t/UdoUEgF6kgyHw3EuF77+Hewmoa8w0HuldldWXezfT5AnjgtH9wzXUp+jR96CgEH59+JlJ2HqDgl21SIOLEZN6J/nhqegUbe3n68H/LcDIAakAHC87aS94fl0e6dEWHrxIi7qeOTiefQ8tZGJm8IIYz504GAWV9Ye7Hy8ycMAAVKOJnF07Om58BqpRwefonCuyzhzJ/LhxHNfBqxuWssE8kEctJcwbdVarBnWdgP6heAoJihPsWFFIeX4D5902kvk3jcBo7nizZTJloTfX4bC19GY4k+LPP0a3NQlPuPbFqv5yV3en3Ck+3H4Qs87JhZPmnDj25Os7Oe//raCqvPdX3camepo6KCLUHYRee/9kLwsKu82Bp85IdJYuKHr5zqIo2gPHp/bsfcdHWXFioL72ZPrsf63KobjOwV8+1wy2EQF8hzpLdISVVJObvWVdz9XUzPjpMwDYu6P9+jKtIU06VGfrqcbbIzkyA1WJ4i/OZM5y5nH9tLazGuuEQO1ssY8eIiQogIZqBztXFjFkShJDpyQH3C8iUgtaqyw90m47n8eDd5MeV2Ixmb+9CNeYI+iLEqjatjngnPzdodFex/qCKM7KaiDcorkzfrkmn38cKCHH6+WrNQU9PoczCasqoyqi56LDTwgKT+8KirxDxxAIMob0jc+04q8N4ZM9vxb1Ga24Gk8GnTV/lt0+FYNO8LsLRvTIdcckmSlx6qm3d8+onZaRiU6qHCvpqOZaS4TJAM7AP1s7qovJ/vJL7rZn0xh7M07FxGNjR7ebQkQvBN6QoOg/bFyaiwBmXNa5pG1xSdoXobJkT7vtGvMPoHdHYJkYjU5vIPG8c1B1DpzvuSn424ccW/4hjpqWwTjB4vV1K7F7w7hu6kgO7C7no48O8Zvlh0j3rz4PlganzGRnMFeX407oOTWY4hcUqrd3YylqyrTdWVxK64FoPU1zFThdL7hVpg8YQrivic0H8rG7vKgShiZZmTMsgQ/umklsePCM2KcyY3AiEsFXu7qnvhVCkBAZQZMKrk5mk1UsJoQ7cGG85NgenLp4nGFTcIdN4UfefQxqJ/0HaA9nX/+QEyFBUZJTx9HtFUxYmEVEbOc8DFIzx+Bzh1NX34GLncH/pfXXl4hIHELifePxzTqOVH2I1XFUPXqY/H+9S9mGL6kr2IcvSMFiqqryxhZti/y7d2pY/PY27tmYS5NUeWzxSAYbDByu690APKfDSUxTLca0tB67huI3Ttobe7eedElOHRKVzD7YUaiqyt69uwGIjev57LxXzJ+BKgUr1mxkW6GmghqfEc2rt0xldFrP1DQBOHfCYEDyzeHO7wTOZOjQoUidnr1bNnaqn8HScU2KUxliPblwCPeU8cu5N3TYRy8Evn6yo/hOez1JVbJ+SQ7WGBMTFmR2ur+i04FrFF5d+4FjEZkjaNB9jfv4SZ9+c3wiWRdci1wsacg/SM2WnYhDEXg/NtFELTVRH5H6o9mYY7pX1e/Xb62kzBYNQIpRz30T0hkyKJbhA2OwhhsZtrGALTU96yFzKnUNDlZddTMjgbgBGR227yphERE4AXzdr2EQKKqqUpXnQhcpe60mxKl8+fEGyhrzyY4bRdag1B6/XnpiDO7IVGRZDu+u16531uCeTzaZmhBDnN7DruLuqxUnnnU2a7fv5MDevUyeE3gya0NYGIpHoKpqQLaoZwpLQRnCg+kObhwwF6O+492Wrh8Jiu/0juLgxlIqixqZcfkgDAFW2zoTs2koOks5Hlfbq3KdzoBU1Far0AshiBo4kgHX3kDG7xZgvt4As+vQN8ZSumRVl+Z0KgP4CoB/nzOADx6cz3WXjmDymCSs/jTPwxOsVEqVqsqeFxYOt5dl37+bkYV7Obbgcqb/4Loeu5YU2kdb9LD3z6kc2FqAcJvIGhfda9dsxudT2bZzEyZh5aa7rui1695+3WV40SELtqIguXBMz6YOaWZCsok8m47S6o4dSdojOi4Ok1Qp7WQUvzk8HEUKGm11AbUvVoYAcGP2BCICEBIQ8nrqF0gp2fppPskDIxkyueu68oiIgQghqSw92m47oSqIDvTGOoOZ+LHTST//InyjS9HnpdCQd7DLc2uqPMqw1Pf5bPZGLlo8stWVz4iMaAD2H+nZdBc+VfL+Hb9m/IFvqLrqZhY8+Wf0ET236rZXad5lppjguWZ2xJpXCgCYem7vR76vXb4Ft2hi8vjpvZofaFBqPNkTzyZBsTHJVIa+l65946zhqCi8sLzzHktnkhwfi0MoNNbVdtzYjyW8OY6kc7bFCEPg6u3QjqIf0FjtpKnWxbDpKd3KMBodpxmkqiva9nyq2rkJxWdBiQi8WEvKBeeh6p1Uvb8bj7Nrq6aCnc+DVMie8IM224wequmyDxTUdekagSCl5J1fPMKkTZ9TOvdCzn741z12rWZseVpm0IjBgVVfCwZZU7Rgt8M7OxeE2V2klGzethG9tDD3ghm9em2AOy6eTZ0llZEU8/aXnU+J0RVmjx1AssnLRwfq8HbTBXrosOEgFHZ3Iu14c6rx6rqOI+CfOaLZP3Rq5+xlOkFIUPQ1ZfnawzdpQPe8UxJTNUHRWN/6jsJVW4ftg1rcERWkLlgY8LjmqERMi4wYq9Mo/O/STs/Lbaujks+Jds0kPDm7zXZJ6ZHEIDhU0nOeTx888h8mfP4mxeNnMvfpv/VK6m9H7lGkEMQO63ztg66y4MaJSJ2HfeuO99o1AbZ/sw+nrGfs8Cnou5l4rysIIfjj3Tfh1FvZs+4LduT0/P0LIbh+cgrVPhPvrN7VrbHGTp0GUpJ/NPAEn1ERsQDU1LcvKC7+5iMePm4B6SZZdC7vmE4IvP1DTnQsKIQQZiHEFiHEbiHEfiHEQ/7j44UQm4QQu4QQ24QQU9voHy2EeE8IcUgIcVAIMaMz/XuKsrwG9EaFuNTwjhu3Q3hkHD5XBA5nQavnS99ehfAaiLo6HUNY5yp7JZ89F8/oYsxHBuM43rncPUVbX0fVO8ga0vZuAvwFVMJMHK7vfgBTayx/6i2Gvvlvjg0ay7xXn+5S6cmu4Ms5ij02BpO194zKRpOB+MEGPNUmSno419Kp7Ni6GyEVzr3orF675plEhJm57abrUYTkv+8u6bhDELh9wQQsipdXNxZ1a5yI6Bj0qo+q6vYLEZ1Kc2LAuoa2VbbXb/qELe4sRugKOTJrLNvnXdKpeen4dqmeXMA8KeU4YDywSAgxHXgUeEhKOR54wP+6Nf4FLJdSDgfGAc1K90D79wjlefUkZkUGRZ8rPQl41Zapj+sLDqIrisc3toy4IdO6NHb0HG1FXLM18DQDqtdLqe1dLM4hxA+d2WH7sckR5Hi91FYHV1isef5t0p9+hJLUQZzz9ovozD3jV38mUkpMJSX4Mns/hcbMi0YhEKz7pP3YmmDh8/korysixpxCmLVvE8gNzUolLG0oYe46PD2cvRbAYjJw/hArOTYjmw90L6Yi3GjA1ongzKgITWXrsLeuTvqg+DBf2VPRq3aWz7yAyE7YJpr5VqmepEZzjgeD/0f6f5r1NlFAC6dmIUQkcA7wkn8st5Syrnnojvr3FF63j6pjTSQPDE5QlEI8Upy+spBSUr10L6rBQcrCBV0eOzptAs6YQjyHAo9CLdn+KW5LKenJ3wuo/QWT0/EBL3/UdcP5max8/EUS//EwxckDmLHkDcyRna+T3FVq8/Ox2O0YR/S+UTljcBL6SDcVh914eqFw0pG9+fiEmyFdqP3cE2jPNYHSS5UF77twMgqSJ5d3r7ZJhDUcL0rAmRLCwrVnh6eVfE8+1cdPD5Wgky5eGB6DSddxFMIP1/2LMa+NYcxrYxj92jhGvzaWgn1XUlb8fOdupIcIaDkthNAJIXYBFcBKKeVm4F7gMSHEMeDvwG9b6ToQqAReEULsFEK8KIRo1vUE0h8hxB1+1dS2ysruFywBqDzWhKpKkgYEJyhIr09EMdac9iE79t77GCtSYFID5piuB18JIdAN9aGvi8VRHpg643j5G+g9MaSNuzKg9hMmpnJOuIVXj5RTF4Tgu5y9R4l95SkKMoYz66N3iIjtueCr1qjYrBkloydO7NXrNjNsRhKKx8y2tZ0vitNZcg9phvOREzqXVaCncNhtuIXhtJT8PUlGQhQzkhU2VQgKyrpeeyQsLAwUBXuANSaiIjUbhcve0q18e00RHl0MV8baOT+t4zQmP934LBvyXgQgLeE8Rmdcw+iMa4kwJxOj9q5jRFsE9G5KKX1+FVE6MFUIMRq4E7hPSpkB3Id/13AGemAi8KyUcgJgA37jPxdIf6SUz0spJ0spJyckdC/4rJmyvOAYspsxm5LRGW3YG5tQvSoFb7+Dsj0J96ACMi66vNvjx0zS0inXbN3WYdu6vL00WXeRZL4cnS5wVc+9i4fTiOSdT9vPW9URzoZGvFddRJjXxYR/P054ZPdsQF2hcbem9kmePr3Xrw0wfcEopPCxb21xj1+rsqISpCAtq39kBXbbm/Dpe1cF9rPFY/Eh+PvHW7s8hsVfq7qpPjAPw/CwSFQkLkdLde3OOs3WMSbAXfRXOS9hCB/Dmms3s3zxP/jfvPv537z7mZowAh3dL9IUDDol9v1qo9XAIuBmoNkdZwnQmjG6GCj270AA3kMTHATYv0coz68nIs5MeFRwdOZh4VqQUVXeHor+/SH63am4hxaRfet1KAFsOzsiKmMcrqjjuA92vNovPPwiQtWTPf7WTl1j4qRUBuh0fJHbvV3by69vAkDVGUgePrBbY3UVb14u9ogILPE9HyXcGuZwIzGDBO5yC1tW9eyuoq6+DoNiRq/v+yQLbo8Xo6uOsOjgLOgCZdLQDEZHevgi30VNY9d2xHqD5rrudpzeX/X5cLu1XYO98aR6WQiB1wBeR8vr5ft3GcOtgcbwKKRHDiLWdHo2ZYvBgsPTt/XtmwnE6ylBCBHt/9sCnAscQrMpzPY3mwe08C2TUpYBx4QQzQrU+UDzN6fD/j1BVXEjhftrSB0cHbQxI6LSiCqeje5/KkplFJ45+Qy4JThCArQPpX6YwFCdSF3u7jbbuWw1VOtXEuOegzkq8Cy4zcxIimSfw4XP0zVD5MYNRTxe7KY+LBZTat+VW1Vq6/BG9a6660wuvn060uxgy/vF7N7QfjBmV7HbnDR4y4mL6Px73RNs2p+HQagMGND7TgQ/OXc4bqnjHx8FHgtxKs0WlVMtFMdz1/HFpxNZs3o8Kz4dx8at01j9yU0nzvsM4HW0XPHXuLWHe0Z4YPm2FH0kTa6WwX5mnRmH91siKIAU4GshxB5gK5qN4lPgduBxIcRu4BHgDgAhRKoQ4vNT+t8NvOXvP97flrb69yS2OhefPb0Hc5i+05li2yMmYSjJB24BIOL6OAYsugkhgqujTTl3AT6DjZr3D+F1tK5HPbbzTaTORebgzu0mmhmWGokLKMiv63Tf/QcruePjfaTodMQMHou3shCp9o3Hhq6xEdnHgiIiysrFd09E6j2se72QT17egKoGL++Uz+vjrRffQwofU6ZOCtq43WHnQU0gnjW29w3rC6cMJ9vi4oMD9TjcnXci8Li1xJnmMG1VX1+Vz75DtyP0Ttz1EejDmlB9Aq/lG6rKNMP5sDnHSc1suQgodbpAqlj0Hddb2VJViPSUI2XLz4ZFb/n2CAop5R4p5QQp5Vgp5Wgp5cP+4+ullJOklOOklNOklNv9x0uklItP6b/Lb2MYK6W8VEpZ217/nsLj8vHZM3tw2b1c8OOxhEcHz1UzKjYDn/DRFKMSM3p80MY9FWNkDKYFRgw1yRx7++MW51VVpazhfSy2wcQO6ZoWb3i2tlU+lNs5o2BZlY2b39iGScAbt00jcvRIpLMBV27vBp41Y3DYkVF9k+b7VDIHpXDDAzMxxDko2uLklYe/wBuE+hh2m5N//vUZjtceJT1mGJPOHh2E2Xaf0uPFODAxNKP37SVCCH5wViY2Vc+zn3f+UWL32xoio6Oprchl46bF6EweRgz5Nwsv3cS4kR8ybsR7AOzb9heO520gNqORzIGVLTyljrk8IBRiDB1rFB7a/gJSGPjz9HtbnLPoLTh9zl6pWdMR35nI7EP+BIALbhtFfHpwXTUVRcEh9KiOwFN0dIXkWfPwDC1CdzQZ3xnVtaqOrMFlLiYl+souRz4P86fzOFhUx45dpXz0xVEaattf0fi8Kvc8u4lGVeWlS8cyYGAMlvFajWT7lr1dmkd3Ubw+MPZOzEZHxMZH84OHzydhNDjLjLz39Jpuj/nhf1fQ5K1m0rDZ3Hr3NUGYZfdRVRWaqlB6sBhVR1w3bwKJeidvbi/H28mswbYmG0L1YTSb2bL+TvRmN8kRvyBz6AL0BiPxyWNITB+Pp3YAHsMW9u770Ym+FbbTnRaMhmgA1lZ0rHKssFdjMsRxVmLL2hRhhjBUqeJWO19JL9h8ZwRFdYkNU5ierDE9k6ffZ9Fj6ETFq65iGReLohqo3PbNaceLc99A8YaRPqnrGVmjI83ogKfyK7j8fzu456vDXPTYGl7872727i1voTqxNbm567G1bLY5+c3odMZNSwcgfJrmpeXY1/Puoa2hqCrC2D+K0gPodDqu+vFcwtLcVB2CnL2F3Rqv6Hge4boYLrpubp+UW22N/QUlmHGTmpbeZ3PQ6XTcMCmRGo+et9Z0Lq7CFLWTESNWs+LDaeijczE6z2P0tDtbtJs4/TF8LgMGqw2vW1Nv5lacLvxnRWjPgY21dR1eV5VudErrixqLXvPE6g8G7f7xKesF6spsxCSH9VyeoWgTJinxuXtWWCSMnY/XUo/96xqc9VqBeXtlKXWmb4hjAYaw7qWsmB+lubP+eUo2fzt7EKqAP+0p5qK3tjHhd8v50V/XsOSDA2xYW8Dlf1vNF/U27hmewvdvGHtiDENSHCI8DteRQ92aS1eQPh+KlAhD/xEUoKlGLvnhDBA+vn676wK0qrQOp6wnLaX3DcZt4fOpvPHuh3ilYL5/kdBX/HDxNCIVF8+vy++UyiYpcw9xSSUInZtw39WcvfiZ1ttlTGD6jE+xqjcwbMJTAJTVnO62vq5Wi9ZOMHasYZCqC6UjQdEP7BR971PXS9SW28kcGdtj4xsTLCjHm2jIbyRmWM+lttYbzERflUXjG1WUvPoV2T+5mqKdryD1XrJG3NLt8f91z1lICWH+ehVXLR5KYV4d63eUsCW3mhV1TSzfrBnTw4Cn5w1j8YKWGVqNGcNwF/T+jkL1GyX7m6AAiE2MImm0kYq9Kgd35zFiXOfdh7dt2A0CRo/rO6+yU9lxpIh3P1pGmLOKhFEzGDmg5wsmtYfZZOSy4RG8dsDNp1tzuGhq++VGAWpqCjAYnCjKZSy8+LEOF5PRCQOZdu7DeLwuDkuw23NPO1+uRoMOrkjv+D1SfU3oDK3b08w6LR4lJCh6CbfDi73eTXRSx14IXSUsPQK5q5LGgvoeFRQA0SPH0DTnQ4xfp5P/1huUJ7yP1TueqLTuGzUtYaevghRFYcDgWAYMjuV7QJPNTW5+LUfya5kyJons7Nbv1TJ+Aq5DG3AcyMcyckC35xUozSVkha73s6gGwnnXTOLNvZtZ+7/DDBud3elcYweO7EWvWhg9seMHYE+SU1zOK0s+RV93DB0K5qyx3HnleX06p2buu2wG7x78kidXHgxIUOTkaEkMszIv6ZTGYX3Os+gFxEVPOe24Dk1FG2Vof0ehqipedxlRka17ifWnHcV3QvVUW655NMQk91yUcOQgTV/pLO2dsqLpCy/FO6IU04FBWGqGkZXVfpbYYGENNzJudBJXXTS8TSEBYD1Xq4vQsOIbPKXlvTI30NQgQL/R3Z9JdHwEaVN0eGtNfPifztVpPlZYQoOrioyEob1anOhM7C4P/3n5DZS6YvTJQ7nzx3fzm1sv7zf/8+gIKxcMCSOnUc/Xuzo2KNfWrsbliiArq3PZdz0+7QGeGX96BgCzogmbGnfLPFCn8rNvHkGoNiYmjm31vMUQEhS9Sp1fUPTkjiI8KQyPlHireu9NTb/2UlTFTUTZNEqPfUTJ9o8o3vwe+d+8RM66f1Cw+RWq9m3C6+z9D1r4tLFgDKPmP3/m6Nw5FP+yd5IDN7s51geYiqEvuPj752BIbqJ0j4u1H7cdQHkmXy/7BqRg3vl9l04c4O7nPseqNhE3ciYP3Hk96Ym9V0UwUH59+VkY8fGPZe173jmddej0RxBiIrpO7kLNxmgA7K7T05PbpKYyyre1n7b8SI2WhPO8tNbjYEI7il5mzduHAYhKsPTYNRRFwalTEI2958pWvXcDimrEE11KrW41B+t/xmHbr8lzPUKR52lybX9id8UNrF03kQPLH0INgg9/oCgGA4m/ehDLpPPQJQ6i8dPXsW3reXdZr1/15GgltUJ/QafTceMvz4VwO3uWVbJ/W26HfdxuDwWlR4g2JpMxuG/zOqVZtMj9Gxf1TS6tQEiItrIwW8cltnc4sH19m+0OHXofRVFJS1vcZpu2sBj9qcbdp8cduRTN5jA9Prvd/teMuh2AA/WlrY8fEhS9i8epfbB1+p69XV+YHoOr98qh29aV4jXXM+qmPzBj8hrGZb3OhMHvMG30cmaO38zkIR8zNO7PhLtGUGp8ncaS4KURD4S4Gy8m+60nyXr9BYTBQvmfH+/xa0ZEal/S5OT+kdaiLcLCw7j6F2eBzsvXr+XgbCVd9ansWL8fVXiYMHFCL82wbYx4UKUgLqLndujdRkpu0i3nB/plxC5v6ebaTFnZCjweEyOGX9jpS4SbtJgRp6fWf0nJgTqtWkKk7Lj+9sjoDADeP9oygBZCgqLXmXxBNgBqD6eUEFF+F9leiKeozdmFsTwdZYILncGMJSaJ+EEzic2cjDVxCObYeKIyRpEx7lqizFoexvCkvknSZ8pOwTh4HJ7jBT1/rYgIfIqCr7bjL2pfk5ASy+SL0xEeI1+8t6ndtjVVdQAMHJbRCzNrH73egEDSYG9fuPU2ufu2suXVX7P7qyXk/HUmU46/DkCyp4iyXStbtPd4nAhlL1KOxtCFwkJWk5b80O2pY2/tMQZ9+TnzdmqlAO5K7zgzwGi/oCiv+pJye12L8yFB0cuYLJpzl6eHH+CGRC1Oo76g5/XjtV/tR9U5SJ4/v8O2dm8BBmc8enPvp/xuRgm3It09/4HX6fU4IyJQS8t6/FrBYOq5oxAmD0U7GtvNBeX1aTtVo6nv3X6z0lMQArYeyO/rqZxg04v3Mei9c5la8Bzj1v6ABFchKzPvofEnB3BhwrXyjy36HD78EXq9m6SkRV26ZoTFXw7V2cDiHYXY9Wknzn0/e1SH/S16I4pRcydOMLcULCFB0csY/YLC5ehZQRGeoaUGaSxovTxisGgqK8RQkIY6vBqjNbrD9m5ZicHbu6mfz0SxWJCe3smt742NQalqu5Zxf0JRBKZUO8IRzo61h9ts5/OXFjWY+t7td/bEEahSsGln36RoOZP1bzzE9OKXqSCWnbOeZ33Mpdi/9wXn3fowEfFplA+8gizbbkp3nr6rKD7+CT6fnpEjrurSdSOMcfgk7LV58ShWLrCWsWpCMmsmpRFtDGyHoihmIiMnteox1hxH4fT2/c7tuyEozJqgcDt61n4QNSgaAOfxwKpkdZXKLzcAkHBeYN4vXuowaJni+wwlKgp8Lry1DT1/scQkjP3Y6+lUtm3bxjH7XqTBxb4VVbjbWMx4mwVFP0hNkhofjSssAUdJDoeLes/1uTU2fPQCZ+f+g53Wc4j69V4mzL+Gs+95jdRBJ1f0yZc+jBMT7pUPnzjm9bqBnXi9ozCbu5b7Ta/Tky/TkP7HaILRzKjoZIZFBr4oE0KPT219AaVTdJh0ptCOordoVj25e1j1FJ5owS0lvuqee2NdDbXoDsbjySohPDmwNA5epQG9Et1jcwqEsIljAGhYsaHHr6VPTcXkdOLox3YKKSW7d+/ms88+Y8jQQVz202nY6zwnPPTOxOdrFhT9I0b2e1dejILkxdffIq8kOCWKO8vetR8wacdvOWwczZi738FkaT19jTEygcpBV5Jl38Px7csAyMn5DIPBSVJi19ROAHafyoO6J3ld9xMAypydWyDWuprwOI4gWkkx3oxFb8HubVlFr7fpH5+6HsZg0bbrba3WgoUQApdeQTR1Ph9+oGxfsZ5MXzSx88cE1N7n8+DTN2IUfZfVEyDqgtmUPxJG1dNPgpBIpxtfYxPS5SL6sgWYBgYvmZw5KxOAmsOHSeujcqjtUVxczEcffURlZSVpaWlcddVVGI1Gplw4gC2f5JM5Ko5h00732lJVv+feKb7+a9asYffu3cTFxTFt2jQGD26ZSqWnGD0onZFnnceBDSt58fnnUNGhl15c+nBiUjL52U2XYu7B3U/u/q0MXXU7xfp0kn/4PnpT+x5YyZc+jPPx9/B++WfkxEUcO/YRCB0juqh2Agg7I+jxRwM7l1Ylt1GLs0iPart+R3+pSfGdEBQndhQ9LCgAfOEGzA09E0vhcnsx74thS6zC5UMCExTOuhIQEpOpb33vdZFWEn/+ABWPPUD5gz877Vz9R0sZuvbToF3LmpWNA2gqKIA+FhSFhYXk5OTg8Xjwer14PB4OHjxIWFgYl112GaNGjTpRxnTS+dkcO1jD2rcPkz485rRSvV6vF+RJQZGfn8/XX39NcnIy5eXlvPnmm0yZMoX58+djNvdOzerrF85ge1YKSz79Ap1ej95owl1fjbt4Pw88XsaffnEnxgBqMnSF0nWvk4WPmDs+JiquY1doQ0Q85UOuISvndY5t+wxVbkf1DicsrHvBggOVUvJUrRTy9ITOCeqRUVo/k6Ft1VdIUPQixhOqp56PcdDFmDE3uPE0uTFYg1ufYsP6Ioa4JH8cZWSuo5IYS8e6UNtxLZjLEtf3bpVx378E66yJ2PfmoI+MQImyUv3869jWfIT7eBXGtODseqIGDcQB2IqKgjJeV8nPz+e1115DCIHRaESv16PX60lPT+eyyy4jMvJ0TxdFEcz73gjefmgzO1cWcfaVQ06cK6o6dKJep91u59NPPyU6OprbbrsNgFWrVrFp0ya2b99OVFQUl112GZmZmT1+j5OGZzNp+OnFKZ96ZzlVBzfx5rJ13Hrx3KBf89ih7UwsfYeDlomMSQr8HpMu/gOOx9/B9dUfMEyxExG7sNtziVY80MXChWEGI1KYsHvaTvtj1vePcqgdCgohhBlYC5j87d+TUj4ohBgPPAeYAS9wl5RySyv9o4EXgdFoJWlvlVJu9J+7G/iJv/9nUspfBeGeWmDsxR2FKTkcChuoy60nYVzwPI1UVcW0qYyjVoWN8TqKnW5iAgg0r6vcAQJiMjtfLtOZX4+7qJGIc9KClp7dNCgD06CTQstz4SJsaz6k4fOviL/96qBcIyo7m1LAXVISlPG6SmWlprv/yU9+QlxcYHVQopPCGDQxgYPflDL1wgEnHDG8qqbOfOaZZ2hsbMTlcnHDDTdg8GfJXbRoEWPGjGHfvn0cOnSI119/nTvvvDPg6waTO69cwP1/2sXhQ4cgiILC5Xax+fmfck7V/0CA6YK/dKq/ISKOkthpZNZ9Q4E3lpEjr+32nCJ1KnhhjL4QrdJz5xDCgMvXtleTRW/51ng9uYB5UspxaP+JRUKI6cCjwENSyvHAA/7XrfEvYLmUcjgwDjgIIISYC1wCjJVSjgL+3o37aBe9QUEoosfdYwHCM7VtZFNhcL17duwsJbPBx+rBKghBlDGwmIgGx04MriQskSkBX6uuYB+Hn3iGqv/soWFZPuWPb8db1zOurRHzZyCMETSt7X7lt2YUoxFXWBiyj11kExM1P/uams6Vlh07PwO3w8uhjVosiK3eRWL5OYzKmE54eDjDhw/njjvuYMiQIaf1S0tLY+HChdx88814vV4OHuzdSPxmdDoFxRoHjuB6nu1458+cU/U/7NLE+oRrGTxycqfHEMPPxaj6sNYJrOHdF6JWHURTx8pZl3Spv1BMuHxtf7f6i+opkJrZUkrZbM43+H+k/6d57xwFtFi+CSEigXOAl/xjuaWUdf7TdwJ/lVK6/Ocqun4b7SOEwGjR9cqOImpwNBD8LLKN645TZRJ4B2kGsDhTxwWKVJ9Kk/4gESLw9OOq18ue/XdQMei/J455qxyUPbaVpi2t56TpDrowI8JiRW0MrmeHz2hAuHonbqMtUlI04Xz8eOdqhycPiCJpQCR7vj6GVCU7VxYhJcw/fxa33HILl112Gampbdd9iI6OJj4+nsLC7lXS6w56gxEhg6fq9fl8DM59jT3mKYT9oZyzf/yfLmXQDR+qCZeYIHlARugFdmlBtuO51B6KYsLdjiD41ggKACGETgixC6gAVkopNwP3Ao8JIY6h7QZ+20rXgUAl8IoQYqcQ4kUhRPNSeCgwSwixWQixRggxpZX+CCHuEEJsE0Jsa97KdwWTRd/j7rEA5igTLglqTfC2i7l5NQwpc1E8PpZyKYikgXB9x+alptIcfMZ6oiInBnytI18/hsdSTqRuAl5rDa7EQqIuGACqpG7pUarfPIAzt+5Ee0+5jep3D+Mq7nqQoWqvR4nqflGp4kN5rH/nc9xuDzq3hyafSkWTDVVV2XO8FF87kc89gclkIjY2loqKzq+Bxs3PoL7CwSu/+YbdXx5j1DlpxKYGHlmflZVFYWEhbnff1FtWFAWF4KXM2fHV+yRQh3fk5dANNWhc1ll49IJ4GRy1cJReh1uYcHi6FjulUyx4OlA99QdBEZAxW0rpA8b77Q0fCCFGA3cA90kp3xdCXI22azi3lfEnAndLKTcLIf4F/Ab4vf9cDDAdmAK8K4QYKM+oXyilfB54HmDy5Mld/uQZLfoeD7hrxm1UELbgucjmryogQwdT5w3gmT1HSRJ1AfVrqNTqBkfGtV2e0udx05C3n/2592JQY2gK06Jth896kLKGrzHuysA41kTSyMmU/2M7jn3VOPZVI8w6EALp36U5dleS+rtpKGGdd4kUOgPS0b0dWFNVLSXXXE2cy8b6J5JJsdup0ut57OlnkNFxWEuLeEfR4YiM5sc33sCg+J6rdngqRqNR81jqJIMnJeKyezn4TQkZI2KYdc2QjjudwtixY9m+fTsffPABV111VZ/UihCdKEXaHlJKBq+/j0YsjJj/vW5OSuAxm9G5grODjdRpj9BaVwNhxo7zO52JXmc+UdeiNfqLoOjUp8evNloNLAJuBpb6Ty0BprbSpRgo9u9AAN5DExzN55b6VVtb0HwHeszZ32jW94rqCUC1GjF5grN6raqyMTjPxsFhEYRZJPu9iYwIC2xsZ1MxAOHxpycDtNvLeP+l77Hi8/GsXjeCHcevxGUupilsLzqPlWzTzzFFxGMdlYVAoWbHDgxxFpJ+NgnrrDT0CRakT4JXRRdjwjggCnyS6re7ViPbOGAUrqN7O1XjGOD40SI+vfdBVj3/Dhv/9SIRLhsHZpyPXehQhaA4Mwuj24W1VPN+Mqg+Iuuqef7559mY3ztqGSFEp++rud/oc9K46rdTOO+WUeg6qWbJyspi4cKFHDx4kGXLlnVpDt3B0ViHWx+c7LLOugpiRBOHrNOwhHctivpUfHoDijc4aslIfxW7WlfXdtQGnQWf2v+N2YF4PSUAHillnRDCgrZr+BuaTWI2muCYB+Sc2VdKWSaEOCaEGCalPAzMB5oLKX/o77daCDEUMAI9Zn00hxuoq+idCEddrBlTrRN7jYOw2O7VwNizMo+BwPB52Xx8bBcuwrg0KTB56nSUIIQBY1Q8u/97iD2byhgwJJrd+2uAW4jM2kjm+A/wmmoZZP1/ZEy6ASH0J1af0SMmUBf1Pvo1kXinOTDEWYi+YCBccLrgkVJS8vAmXDl1+Bpd6CJaLxbfFubRo3Ad3IC7uApTRmAqgeUPPUHW2/9hEMBy7Vhuxgguf/lxUlbvRu/xcHTeBI6UV/DVvoPcOvts9hQWYfOqrPr4Qz5+602811zLrCGDOjXXzqIoSrvJ/nqSGTNm0NjYyIYNG4iMjGTWrFm9cl1VVdG5GtBFB6d+dtG+dQwDxNTbgzKe1OtRvMHZ8Uf5s87Wuru2IzbqLTS0k2Leorfg9DlRpYoi+i6RRiBXTgG+FkLsAbai2Sg+BW4HHhdC7AYeQVNFIYRIFUJ8fkr/u4G3/P3H+9sCvAwMFELsA/4H3Hym2imYmCMMOHqpqJA5RdMl1x+t69Y4Drub9P117M20MDw9mg8qaoimlnPbqIjVor/nGAZnEp//YTPr15bQ4Fb9QgLCo2ppKJrKuElfMH9eLtlTb0GnM56motDpDVgvSEDnjKBszRdtXkcIQeQ8zeW17LFtqJ7OqfiMgzXB4zzYcQGfZiI/fheAxr88ycHzriIvaxQj/v4Xtm7S1G1egwGzXs/YtFTuXTifSLOJs4cNYeGoYdz4/e8D8OUbr7Fl3dpOzbWzSCmD5lrcFc4991xGjx7NqlWr2LNnT69cs7CsBhMeEpKCE+RZX7ALgAFjZgRlPKnoIEjCO8aoLQTru5gZOUwfhk9tX/UEfZ8YsMMdhZRyD9CiWoqUcj3Q4oklpSwBFp/yehfQwo9NSukGbuzcdLuOxWrAafMiVYlQgvPFtTntmI0mdMrpGT0jsiJwrAdbUSNMDdwt9Uy2fpXPQC/Ez86g1FbFRmcSF4flYdR3vEtx1VdQr9tL7Za7qKhyMjjDysi56TRVOhi6OJvS4h189Gg9m5etZuHNl7c5TvzQ2ZSyEY+jfXff8BkpNG0oxVfrpP7jPHTxJuyb1hI2cQgR505t92FpHqIJCvfRPFjQdiS1rb6Jb559A1lZQaatlsPX/IhLLzuPqZedd6LNP3//Bub0NFyDo2loaCQysqWqYld5E+a8g4ikND5f+SUep4OZ53U/+Ko1+lpQKIrCpZdeSlNTEx9++CEGg4ERI0b06DV3HC4AYEhWcNKyqPYanNJAXGyQYkKErt38Sp0h2hQGOKjroD52W+iEGcVXy3MfP8GxpmOkh6dxw/xbifRnhW4WFHavnTBD3xWK+k5EZgNYrEakKnHZvZit3c9Bc6y8lGs+uYaZ4XN57LqHTjsXNSgaB+Au77qqS/X4iN5WxYEEA+eNSOSvez/FSwZ3DGy9EPup1Hx0BZ7Kw9Qf/j4VjcPISDCz4P4ppz2w0gdMIW7gy+RuSaV8bh5Jma0XNdLkuebG1x6KXkfSzydR8oeN2LZq/v++Bj21r9yCYeAEwqfOoOGz99CnDSRizlwiF52DebiW1NA0RPvtOd56DYniw/ns/OX/I6I4nwz7yUR/gxafXoujuqaRL5wRjCs6yuahUzmw/xDTZ2jOdA6Pj8c35/PfjYVkF+xkvq2OcWdfw5ZNm1i5fgMOh51zL76s3XvsKn0pKAD0ej3XXnstb775JkuWLOG6665rEYMRTPKKNNvYpBEDgjKeKgzo8Wm7gCAY5aWiIIK0o4g2WgEH9V1UZamOKACern0Jq89Ck8dB/jt5WMMj8EmVQlX7XzrcDui5Ss4d8p3IHgtgidCEg6Opa+onVVW58KUruPg/V1PbVM/dH/2MRkMtR+0ts33qwww4AbW26waz7RuLiXWqcFYKQghW1akMUEoZn9D2alB11VP7xR3E7vySxGPHaLRpD+AL/9D6iv68m+YjhMqqNze2aexsyNcM1Prwjlczil4halHWiVQTusg0TGOvxZO3g7r/PY30efHk7aH6uUfIv3QRB4ePxJVbjOJPdaK6W3c22PGHvzHwyE5MQlKYMRzXP56l5vd/Y8y00+NDXn91GW6dge8N1+wcOXWagbHB5WH0X77kxU8P43R4scZoMSiRyQO46977sEgf67fv4pO33+zwHjtLbxuR28JsNnPjjTeSmJjI0qVLOx0E2BkqinKx6SJIium+4RlAr4BA4nQGycao6CBI70usv+BQo6drgsJsPo9o68OsvugrNt66hSGuLJbp1/C+8zO+cKzmsOsoye54wmr6dk3/3REU/oeRo7Frb+je3MMU6o+Qbz7IOe+fTa5pHxZvBFWy9Xz8HpMOxd61a0kpkRtKKLTqmD01Hbe7liPeZMYb2/fHr/v0BmI2vKP9feGvGZ+lldcs/OyzVtvHJWUxfHYDtUUp7N+8rtU29Xs134P46YHVvog4O530v8wi9ZGZKKYSjAPnEnHBbcR8/16GbljF0O2byHjhTRRrIiApvPEW6j78EoCSIzso8K9Gm6mtqCFt32Zyxp3NjO0bWbTyA8YvnsPMGy4+fZ6lFbxV5GOcp4pFF80D4Ji/VGejy4vP7iUyJZyD95/Hzy7WdN1b164nOi6eH//il4Qrku2Hj7Lk5RcCus9A6WvV06mYzWauvPJKvF4vTz31FF988QWeLj7g2mLT/lzCfY1kDA6eeiu57Gv26UdhDus4yDQghBI01VOkwW+P9HbOLuf2+Ph8xzF26CXDLYOIi9UWNy9f8wbXWi7ljWmvsPHWzaw5/yteyX0YS2NIUPQK5m7uKNbnaA/dIb4xpLoHcHXkzcy2LKReX4Pb18qYEUbMPrVLK8rDe8pJrfNSNSUek05HUe1+3MJEfCtCSUpJzRc/wPaPNGL3ag/7uonnEzPlftIW3QJAbVnbwUBnX3wJRmstmz4o4MtVP2Tr1mfxnrKNVp0qqt6BKaJzcQeKopD484uQXidSTSD5Nz9EMZtQDHqssyYx4KP3iDj/Bny1xVQ8pKX4iji8G8eC81j+yps4HQ5eu+hGys6ZicnjIurqK9u93l///RFV5kh+c8UkwqMiibQ1UurxIqXkF19ou6K75g7CpFeYMG4EjZYYnJs+Zc3j91K/8wvu/tVvidIL9hcd581nngqap1J/EhQA8fHx3H777YwbN44NGzbw6quv0tAQvHQzn3yxBq8UXHFuYAuLMynJP8jOp2/i2IFN1NdUUnFkK5neQqozg2dDkooOoQZnR6FXFCw4aPQFNp6qqvz6iwOMWL2HW+urMUr45biTdWWio2L43dV/ZOwIzfyrj9FUvt4gBvB2he+MoOjujmJfzV5MXgtLbn6DFbd/zO8v+wWZEelIof7/9s47PKoqb8DvmT6T3hMCJKHX0AJIEQRBaYKoK9gWy7q21bWs5XPVtay7uuq6zXXFBqwFK6isClIVadI7BEggvfcy7Z7vjzskhJlJJiE0ue/z5CHce8+95yST+zu/TkZBltf1hmgrJiGoakMpj8LVWZSYBWPGJAOQHHMRBumk0uBdAbZ8x6tErvuEoMpqnEYd5RPuJnz6Qlx1dpa/uwerroJeV4z1+yyj2UqfsRJ7RUfKD7uorHqZb5de1tD/QLj1SNE2gWcItSFdpSC9y16bEmPo+OoTRN7+KNbJNwCQHaNGySS9+DwZgwYzLH0LAO/0ncotPzk5UOpb4O3+cRsfueK40lTKiJH9EEIweflnJC5+h4G/+5D1m3OJSwrlztRO5BzZR9ZfLuLWGLW21OZNh1j133ewWK3c++jjRJmNHCosZt4/Xm0XYaEoyjklKECtQTVjxgxmzZpFYWEhc+fOJSvL+zPcWnYdyUZffgxDXDcSY8JbPT7/6AEM86cwqOgL4j6aQtg/uhH7gZrDGzOs7X0jTkbqdO2WDAgQRB1VASgUxwqreGrlAeYbHYxw6nkjPJotl6TSIzHM7xidxYDOZsBdpgmKM4LV48Cub6NG4RQODIq5SfhoclQyAOl5R7yf5ym3UHlCuYtAyMksp0tePYf6hxNhVYWbQWcgVldBnrOpE95ZW4Bx1Su4DDrk4zkY/6+E8NFqRc2Nr39CaX0ME6bqsCU0H30SkaDuXlxyOHV1HbFaj1FcrIaZ6mON6J1BVGUcatU6ABzH8hGmOAzR/vs8xz10M8mvPkH6gMFYXU5ifvqJkr//i0PDRnCg30CmXvMyn3e/BICDZb5t1C9/9hMWxcXv75nacKxz3iGM0kX3mkNEdQzmi18Oo66mGtd/ryHGnU922r1ccd/dpES5OW41MBiN3PPIY8QHWTlWXsX81187JR+D2+2mrKyMiIhT63lwuujduze/+tWvMBgMLFiwgPx838EEgfLJ16uQCOZc2frdf2VpIa75V2LGzoY+v8ckGt+8B4096derdU2BmkXo2s1HARAkHFS5m3+V7swq45Idh3hLb2dijWDB5f2YMagjNlPLPdD1ERZcp+DvbA8uGEGhN+owWfRt1ihMioUaUznl1Y1qes8OaqTQ4cIMr+tDUtRdQk1262rApK/IoFYPaeObRoxE6uoodTf2t7AX7UD3cg+CK6qp7DcWYQpuiAipPJLOjoNx9Ek8QuepLVe13LfpGBLJ+OlXk5L8WwCKilTBEDl4MG5jNWXvZFC0dW2r1mLPKEDo9Ji7tRzW2OHWW4gqK+X7f/yX0ZdfyhUL3uHKTz9k9SPjmT1RTYoLMXvbaZctWs1qUyKXWyqJiWs0jyVaVHt2L30ZW34zlvggMzvee5QkmU32pa8zctbD9Bg1BWEwNgmk0en1XDP7Oowl+RwtKjmlrObS0lLcbjdx7ZRPcDqIi4vjtttuQ6/Xs3Zt636/J1JT78BZfBR3aAdSOrS+wMK+9x8m3p1P1uXvctG1j7DVpEaruaWAobe3r1bWjqYnAJujjuJavVqtwAd5JTXcuTsTmxsWdUxk3uT+rSpoaIi04NZMT2cOS4iJuja0Kf3pwHbWu1bS2zWYiJBGNbFLx07oFSOZpd7lIEKSQpFS4mxFNnhlSS3Jh6rZ2T2YpMimBeCCdS5qpEcryt+Ee/4k9AqUjvwFETMWNbl2z6I1gCBtzkQCofBILcJWR2xCFOHh6ku5vOKguo4O3Yi4MwW9y4r9Y0n2isVeJhlHdSXFO9ZTdmg7iqe3c/mSDZR/qfpU9GEtR0yljhqJWxgIXb6F7bnlDcc7h1qxexL4wizq+u0uhas/3sKcxdt5aY36s3/olqZlxiLC1F18WPEh8guKObpvM2m5H7Ax4gr6ekpCSykpqXAQdlIm+YH1P2ApymXIwIFs2rSJb7/9tk3C4ngxwOPlxs9VQkJC6N+/PwcOHGhzEcEvvt+GGRfDBvuvK+aPI7s3klb8BZvjrqHfCFUb6XHPRxT/ege6p8vocVn7ZGQfR+r07Wp6GuTI4KCxM2v3e/sQ07PLmbj5INlG+FtCHCO6x7S6HIs+woyrrB7ZjsKttVwweRQAoVEWyvJb5zMorSrjzh9vx6zYeGnan5qcMxgMRLpjyKn1LiOtM+mx6wSyInCVcdvyIyRL6DYuyetckM5NrcuEq64I5k/BbHdRMfOPRA64t8l17toq9h2OIik6h5DOJ9do9KaitBp3tYmo3urLPyGhHzt3hlFX9zWK8ig6nY7QxF7o7jVR+MEWTN/Fc2Tffwkf0wN7fgn2/ZUY8uLRSSNQRXnIF4RdlUTNysMIU2cUewWWvj4LAzfh8NzP0EsXhTGDWPevnST83zDiQlTfxvHdZEG1ndIgMxPf2UBJVhUgwRLNMylOOnZtal7TBwdDAeiQ7N62E9vhD0lAR4/rX2q4Jm/TMirqDQzr26fJ2Mztm0ns2ZtpM2ZgMJvZuHEjbrebKVOmtKq4XkFBAUIIoqPPbr/yQOjfvz8//fQTP/zwA5deemnLA05i1+5dCPRMHuWVm9siRSv/RTwmes9u/PsKDosiOOw0NV0S7SsormI33yiDeeYoLO0di97zGflkXQbPVZbj0sGSbkmkJretEKUhwgJuiVLlQB/WuvI47cUFpVHEdw2jJLu6VcUBl25dg0NfzwN9HiYpPtHrfIIxkXyZ5XPH6bIY0Af4LEetk/hd5WzrZGFgkvcHyqqDKmllwbfPU+2yUXfli4SdJCQAjiz5hjoljH7jA6thtO2HdASCXmnqi1an0xMRcRVWay47d33VcF1wYheSH7wKRpRjyu1E/Qcu5MowdGXBuPsWYLzGjZhQiXAZqJ1fizB1RpgL6fzqNMydmze9SEVSvegT6oPjiL3/CiLKXPznX9saNIkreqk9kZccKOByj5CwRJoBQUJ9OdfNmex9U32j7ffo4SN0K1rOvuDhRMQ0Zsrv/t+HGIWbXlfe0WRoeX4e0Z2SEEIwadIkRo0axebNm1m8eDFud2BhkA6Hg61bt9KpU6eGLnTnMp07d2bgwIGsXbuWJUuWtDrPwl1VghIch9nUurXW19XQu3Q5e8LHEhbZfh0hm6WdNYpgvYXrXZ+y2wrvrc/ErSj8e+1h7rVXEIbgg+6d2ywkQHVoA6eteVhAczhrTz4LdOgWjpSQdyTwzls1nnLE3WJ8Z5n2COtFmamIwiIff1ihJqyKxBVAjPWWNZkEuSThY7yFEUCk0UQtNh4Pv5l3+9xCcOodXtdIRWHbejvhpiI6jw0sPDFzVyGKzknfoY2N4QcPvhe328ixo+82uVan19NxxhXEPtwH40w3wb8Oo/OT00i58Tri0i4hccJU4u9tLCKs7xdY3Pux77YQXJSOdepMJozsTOjkjkQfreMf76q1icZ2CkcXZGDJqkyKsqoYN7ITt8WpNtubupkxWXzsshQ3SEm1PojCfZuJpRTZr7FUiaO2mv3phfToZMYU06jBKYqb+ppqbGHhgKrNTJgwgfHjx7Nz504+/fTTgMqG7969m+rqasaPHx/Qz+BcYNKkSXTv3p3Nmzfz3nvvBWyGUhQFk2LHGtT6PIc9qz4mlFosQ85YNZ92FxR6fRAjxWoG1cMT9RWMWrqDZ51VDK+FleP7Mzil7ZpR/eFyyhYfQh9mxhhz9lKzLyhBEZcSitAJ8tLLAx6TEqs2b8/IP+bz/IAO/UFINh3a5nXOGGPDKASVWc07tBWXG9umQvZEGbi4n+/aUM8MmMJ9ZrWJ4Pi+U31eU7JrJ0V1ifQfpCACsIMqikJ1Hlii3ZhO2AmaTGGYjJdgte0hJ8e7naYlIp644ZcQ3iXVyxRjjojjiYSj1G38Nxu2/tjiHAAqVqwGoPOv1R7Gc6b3oCI1DOvWMg5mVaDT6ejfXf1ji0gI4q2pfen36ds8u+1dbrttms97SreCAByhcZiKsnll38XkrviaBbdN4ssHZ5KzbglORUfKwKblyuy16sbAbGv0EQkhGDNmTEPZ7oULF7aYqLZ161ZiYmJISvI2I56rWCwWrr/+embOnElpaSmBNgqrqK7DIBSCg1svKJSD31JGCH1G+v5MnxZ0BkQ7mvsN+mAw1PHvIV2YYNcTpQhetoTx2aRUjAFENfnDnllBybw96MPMxNw9oE29XtqLC0pQmCwGYjoFk9uKqq5JcapJZl+eVxV1AIZ1V9tr7Mzb7XXO2kn9w6lsQYPZtTGHqDoF54h49H6iO6wGA0ZPVMXADr61m/3LtqLDRdcAk50O7c5CuI107O0dvtmv/30IobB33+sB3es4u0rS+Ta1H18lh5Hy1SKyjmS2OMaRlY3TGIQtsdFEdc3VPXDr4IslavTVXyf1YfCgeBbfPIytK9eQnL6fyBmXY7Z652iAql0h4ZpfNzpC92U5KKo2kJ7jZMtitdWrclINq9rycgCsod5NaEaMGMEVV1zBoUOHeO+997D7abVaWlpKdnY2AwcOPOdyKALhuKlMrw/sJWd3ujzXt97lmVixjQzbAPQBdGxsN/RGtcpMOyVV6g3q33mixc27U1L539SB3DgiBUMbWrUeR6l1UvzuHvShJmJu74/hLPkmjnNBCQqAiPggqloRavbaarWkQ4Wr3Of5DhFxhDgjOFDh3bQnzBMiW9tMiKyUEvvaXI4G6xg3vHOzc6l2qvOWJu+WmPVFhew7EkOX+FyCOgW2i929PhOAgaO9C8RFR/XB4eiJ270KtzvwSLGNJWriVt/7rgcEW/7xrxbHKIW5uMOa2qd7xIVQ1TsE4+4Kckpq6Rpu4/NZQ0gKs1H8+uuUhEcw7mb/5grpdiN1gkijm5tee4+r7/kVN//h96T1Uv+ojxYp9EoOpde1DzUZV16g9gUPj/Ot2Q0ZMoSrrrqKY8eOsWDBAurqvEtE//TTTwD06xd4r/JzieP+tkAd922VhcW5mXSQBdgT/VcMPh0InSoIpa+KCm3AYFQ/U87a9slwl4qk/KsjSLubsMkp6ENMLQ86zVxwgkJnECgBptsv3bya5Y4vGeAawfO/eNzvdZ1EF466vXsp2BKDUSS4iv3Xm8/YU0SHMid5g6MJMja/g9tm1zO4LhOD6aRwU0Xh4II3ccggBs8Y2Ow9TqTgcA1Y6kno5DsqJzp6EkZjLYcPr/Z7D5fi5o39yyitL6e4roQnc1UT0bA+g8i8eCwdV6+kqqb5EGF9eSFEe7+Yp0zrhsENHy9p1OZ2rPmR5L27KZ59A0E2/zbb5MhYbHYnn/7xCXZ9+SGJwycR1WcEY556n4tH9WDSjLFMfn4B4qRdc3m+R1DE+y8Pn5qayrXXXkt+fj7z5s2jurpxI1BZWcmmTZtITU0lLMx/xu25zHFB8c0337BixQpWrVrFp59+yjfffMMPP/xAZmZmk+uXf6u2n2mtNSf7wGYAIroG1l+l3dCrL17pbp8igwajWvzQZT91QVG9Ppecx9dSu62Q4IsTsfQ5TZFfreTCExR6XUCC4oedm/jdnnsJdUby2qxXMBv9q349QntSaiygrLKpiUnodTj0Air9RyvkrDpKiUkwZmxyi3MSUsHka/f23ZMYCtRyF8bEHi3eB6CyrBp3pYmIzv53K716zkRRBEeO+C+U99KeZfwhL5Znd6/iv4cbfRIhRhuJV15JUH0d67/6xu94t9OFubYEXZx3N7TBKRHkdneyvmAuWWUlAOT8+3XKQsMYd9ucZtcXYbEy5kg+gydPZ/vS//HVX/+s1l3S6xl231/pe/3D6E4yd0gpKcvLwWwLwhrSfP/j3r17c91111FSUtKkXtL27dtxu92MHeu/bMq5jsmkfiYyMjJYu3Yta9asITs7my1btrBixQrmzZvHqlWr1J9XWRnpB9UKyq01s1VlqwUnE7sPbNf5t4hHo1DaqRe1waQKCmd929qhnojTU/InfEZXwqd2abfeOafKBZVHAaDTt6xRlFSVcfe22wB4oN/DhAU3Xy55QIdUFh/6gA0HtjJ56Lgm51xWA0Y/SX6ZR8tIyaln05AIBgQ1b4OUUpJhiGCsPKmC7PrXYP2/OGRWX+aBhP4qisInf1sLGBkwxn/PgNDQTkg5HoNxJQUF+4iL864I+nmJ+kFeWJWCqFTors9h5ehJGHR6Bl06lo1h4dQt+QpmX+3zGfV5xeilCxnrO4T2UOTH7NNt4qmtb/NowgxSdmxh/y9vZWRIy45TvZSMu/nXhMUlsGreGxxY9z29Rvl+gTvqalnwyL1UFBYQk5QS0EuvW7du3HTTTbz//vu88847zJgxg7Vr19K9e3eios6NnWBb6NatG3feeSdRUVENrVyNRiNutxu73c6yZcsahEdOjppDZJd6QqNal1goig9QTgjhkW1v7tUWhEejaD9BoX4WXY5T1yiE1YAw6gge0T5tZNuLFjUKIYRFCLFJCLFDCLFHCPGM5/hAIcQGIcR2IcRmIcQwP+PDhRCfCiH2CyH2CSFGnHT+d0IIKYQ4I1lJer1AcTfvxLrjEzU/YYbleq65yHdUzYmM6Kmqztuydnid04WbsSKx13kLi/1Lj1Cnh5ETfDcNOpHNBzZQaIxgmO0EU8nuz2Hp49B7OnmVsXRLiyU2qfmdMMD/5m+gvsBE/AAdfdOaf3Zq6v1ICZt+ugOXq9Gmu7eqlvhV28mS8fTQ5TDYcJQZwcf430VjMHrMOXqDgYJLJ9Jl62YK8nzXEarNVrNZjTHev/6dR/azX6j2/s15X7J1/ru4dHpG33JTi2tsaIoBDLp8KlEdO7Ppy8/8Zljv/WE1FYXqXAzGwG3CSUlJzJkzB7vdzvz58xsS885ndDod8fHxGI1G9Hp9E+e2zWZjxowZpKWlcfjwYWJiYrjyhtv40D6YyJj4gJ9RXJBDbOUeiixJbXdytBGh8wgKdzsJCrPH9OQ4dY1CGHRIZ9uKcJ5OAjE92YHxUsoBqD2vJwkhLgL+AjwjpRwIPOX5vy/+DnwrpewFDAAa4i2FEJ2AiYDv2NPTQCCmp0SrGunkUgJLlkuMjifEGcG+cu9QUmOsDYMQVGQ03W3sOlBEnyO1pPcJJz6i5RIXv82sJNZewuR+o9UDlbmw6E7oPAKuepOQCAtuZ8tRHFvW7OfoxjoMUbXM/PWYFq+Pj+uDQT8Wmy2HtWufbjgeoW8UfE93i+fri2fwn2FXEmpuqn31u/F6DIqbDe8s8Hn/+jyPoIjzTrZ6ec2r6BUjV/V4GNwV1B9YRsZFo4hJaPmFJJ1O8LzghE7H4CkzKMo8QvbeXU2vk5KtX3/Bynf+Q0SHjnQbehGDA6iPdSKJiYncdNNNhIeHc9VVV52zRQDbCyEE06ZN49FHH+XWW2/FFKT6Yiwt+NiO8/1HrxL9eh96yAxKE9uYZ1J0ED6/AyrzWj9W7wkzbSeNwmhWN2cuZ+vquvlCGDyv5AD9qGeKFgWFVDn+EzB6vqTn6/j2NQzIPXmsECIUGAO87bmXQ0pZfsIlrwKP0Ho/WJs5bnpqTmL//boXiHV08tm9zh/J+m5kur1DaIM8O/yqzEZBoSgKxV8dptIkGD2jZZ/CvsObOWKOY5YrnegIz0vyyGpw22HqX8FoISIhiLL85p1ze7ccYf3CLDDbufbBiwMOfxw37m3q65Oot39FfZ3aijTBFkY45Uy1HWV8on9nZPc+vUhPG07HTxb61CpqjqnHgjo3vvxLK8v506K/sU23jsts05nRU32ZVNrqSbjh+oDmrNTUoDuhK1/viy/BGhLKlq+/bDgmpWT5W6+xav6bdE0bxo1/fpUZv3uCXiNbFqAn06FDB+6//3769u3b6rHnK1arFSEEdZ4MemsLOQPVtbXkPd2VMfueBqAeE2mznwzsYS47bJyratHLnoDXhsLOhfDuZCjxDiRpFoMaUq04W98CwPft2l9QSFf7hO62FwE5s4UQeiHEdqAQ+E5KuRG4H3hJCJEFvAz8n4+hXYAi4F0hxDYhxFtCiCDPPacDOVJKb3tN02f/2mPa2hxoAlBz6PSqmqs0U2BLSold1GLW+Y7R90XPsN6Um4ooLC1pcjysWzgA9TmNH6Lv1x2je7GTolHxhAa3HB+9PE+Vwb8aekLtpmqPryJCDYWNiLdRUVSH288H7PDebFa+nQ56N1c9mEZEVOsicmLDZ2Mw1LFx6WUoLjVM1ypc1ATQK7j3449hcjhY98cXvM6V7VT/yKNSVRPY0YIcpn98JR9Wvk2KszePTb2f1PAEjC5JbqRgyLiLA5qvUlOD/oSkOaPJzICJkzm8ZSMlOVlsX/o/Pv/zH9i5/FsGXj6N6Q8+jslyFpsSn8fUHxcULWgUB9cvIYFiAPYZ+6J7PBd9oGa+RXfANw/Dp7fAun9CbB8Y/yTUV8DccXBoRcDzFQb19yxd7RT1ZFO1aLe7HQSFUX0/yQCsA2eSgASFlNLtMTF1BIYJIfoBdwEPSCk7AQ/g0RpOwgAMBl6XUg4CaoDHhBA24PeoJquWnj1XSpkmpUyLiTn1WjANgqIZ1W7V7h+pMJYwKGJwwPftGa3mIuzLbtq3wRRlwQEonhDZmhoH4cuzORZm4OJLW/ZNuN1uFlYb6eIoICb6BMdzbYm6MzKqu+aI+CCkIqko9FanHXYn37yumlym/qY/HZJaX83UdbScw4eG4got5eCKKUhFwSgUHAHYUrv36cX+6VfRa8VSVnzXNFu7IrsEt86I0bP7f/Lr56jRVfJy33/w5a8+JiIkjAPbVuA0CGIqZMCx/apG0TTfZMBlU9Hp9Mx78C5WvPM6xccy6TNmPGOuvxnRimJ/Gk2pc/gWFFJK9ny/CJ4OY/Pcu6nL3NxwLvTKl5tUA2iWskzYswhGPwCz3odZ78Fd62DM7+D2lRAcA1/eC/bAfASinTUKvckKig53O/g8jldUOC81iuN4zEargUnAHOBzz6lPAF/O7Gwg26OBAHyKKji6AinADiFEJqoA2iqECNwb1kb0HtWuOUFxIEfd5TpF4IlmNs8Lu97ZNJlPCEG9SY+xSnUEr/1sH5H1EsuMLhgMLf/4j+Qf5rA5gXvCnOhONBXVloItqsERGJmgvhR9VcfNzypFOM2kjAwmpZfvWlLNIRWFrYcK0JV0IEnpS47xKEdWXc3g9WuIWb09oHvsuOxq6kxmDr0+r8nxiI4R6BQXboeTrMI8tov1jDVP4vK0xuixTzbMRe+WXLwvcKenL0ERHBFJ92FqLEXfSyZwx38WMPmeBzFaAtccNbxpND01/Tx///6f6bvyZgDSct9nVNYbAOwa/RqJfVvRKnWPp4z+wBug9zTofUWjAzwyBa58XfXZrX8toNs1ahTt46PQ6XXoFDNudzsInuPhsGexpLgvWgyPFULEAE4pZbkQwgpMAF5E9UmMRRUc4wEvA72UMl8IkSWE6CmlPABcCuyVUu4CYk94RiaQJqUsPvUlNU+jRuFfYq/I+Q6DMDKow8CA71teo/ogwoK8Q2llpAVbXjXbd+bRe28lu3qHMLVPYM1s8soLgBC6hp1UfdJZ22BrBQiPUwWVL0FRUaKqxGFRLTvNfZGzfQVF7hCm9Y2m6/iXcCwbS6ZpJ923qYL0D/ffgzWlGyUF+XQfNoJfX3ml1z22uXVEDBnBuE3ryM8rIT5BDR8N7p6EWC+pSj/GB+lfI4XCjUNnN4yrr61imf4AQ9MhrFrBkZODKbFlYeeuqcEY773vGHPDLYTHJzBsRvM9uDUC57igONGZ7Xa5GHTIOyv/sL4LianjvI775cA3sPxpiOkFUd18X9NpGHQaDodXwiWPtXxPj6BoL2c2oAoK2kOj8JiezjFBEYhGkQCsEkLsBH5C9VEsAW4HXhFC7AD+BPwaQAjRQQjx9Qnj7wXe94wf6Ln2rHE8Pr45jeIYhxliGM2koYEnTWWXq36EFB9tR62dQ9EJQfQHhyi16BhzTR+va/zh9PhJDCeXGzBaVQcf4Kh3IRVJcISZch+mp+oK1RYbEu5d+iMQtq1biQEn/SbMRuh09J64in6hN9IpRq2YG5yfhXHdCuIP76Hqw7dYt3ev1z0ynU6+HjwWq9vBurkfNBy3dlF9LJX7M1hetJR4RxJpPVIbzn/97T+pssI0+gNQtXRpQHP2pVEAhMbEMnr2LzFZ2yY0Nbyp9yEonLUVhFLDDwk3w9MVrO3xKNvSXqDrk9uIjG2FVrtlPgTHwS3fNB9G22EQ5G6H4pZb9gqP9i+d7SkoLLiVduhCd45qFIFEPe2UUg6SUqZKKftJKZ/1HF8rpRwipRwgpRwupdziOZ4rpZxywvjtHh9DqpTySillmY9nJJ8JbQLA6bGnGs2+HW/2Oid1hmpqlNbFRGdVH8XsthIb6u1HCe7TGC7pmJ5CSFDgcfphnh1GycmCzWBp2BEt+dcO3nzgewCqSrw/rFWeXtNhka0XFC57PbuLoU+4E0uomusg9Abi0p4hNrY7Rp2b6/49j3HP/53gGdcBsP6ZR1i5aVPDPaSUlBmgtktPCmI6YV32FW7PH0JIT9Xvsn3nevJNR5kQ07Tf8uKj/yOmWsek36gNh0reaVr63B9KVRW6AJLyNE6dUE/nwaKqxgoE5cXqxqnepGqOo69/nEHT7mrdjasLIX0ZDJgNthb6OYy4G0xB8G3LGoUweARFe2oU0oIiT/1+De1Uz5GM7ONccB48Z70bRKOgUNwK+9fnkbW/FJfTzY8b1SCsIfGtqz+T48wmRknwmdF7rNLOUZtgQbKRYYNbl3G53/MH1yXqpHEGC7js1Nc4yTuklg6pLrOT66OEeo2ny15EbMvJeCdz+Kdl2DHTv793gbug8Aicip4opY7B3bpyx/U3MOix5wDY9sqzDU1+9pbWoBh19Am2YLjiSpJLsvjxa7U/c2TfJJymYBYpy9ArRm4Y9YuG+2cc2MT2iAqmmoZgTuyIdfBg3MXFVK9Z0+ycpZS4q6rQt5BRr9E+DOgUDsC2Y+UNx+KT1TBhY7V398eA2b8EpBsGXNfyteGdYcgcOLwCaprfc54uQdEepidXQQ3oBIbws1st9mQuOEFhr3NhshgaXujrFh1mxfx9fPm37bxx7xo++F71z09MbV2tnmJdHrE6b5W6NLeGLe+nc+swG5uHta6sg1QUPi130dFRRLcOJ1V41RvB7cRe6+1wPzlHpK7aiUQhJKz1GkX6nm2YsNNluHe2cUyXngAU7VrXcGz8oEEUj1a1gmef+wNSUfjmmBoyPCYujLTbr8ehN5Lz/kJ1GSYjB4f2ZnO3ciYaJtHxhOzej1f9EyRcO+F+AOKf/gMABS+82OycZX09uFzoQjVBcSZIjrKRGG5l6Z7GPJlNH/8ZgOEli9t+4/TvIDxJ9U8EQo9JIBX44ZVmLzvuzMbdfh3jdFhROHXTkz2jElPHYESAyYtnigtOUDjrXTjqXCx7ew/fvLGLHcuz6D0ygUl39GP49C5sTFLbf0aFBJ5d61JcVOvLiTU1dVA76lx888YujGY9o+LCyHcE3oJ1Z8ZOBn23mg3WLtwVVO0dvqk3gdtBWLSVxB7hRCUGc/MLo5h27wAvrcZe4wK9q1X9no+TWVRNkqUWfbB3iY3Y/mpOQ+H+LU2OP3v3PVSERRG6bztlhQWsK64CKZmcFI0lIpzCIaPpsetHsnJVAbKmbx5B9fDo5b9puIfLYecb9w4GV0TQKUX1WVh69MDUtSuOjAzqdnn3/ziOu0o1G+rb0EhHo/UIIZg5KJEf0osoqKxn74ZvGbZfLdSwM/nmtt3UZVeTSrtfFniJj84XwdBfwYZ/q2P9oPP4KI77+NoDPRYUcWqCQnG4cWRXYU4596oOX3CCoudFCST3jyL3YBlHtqkJfGOu60HXQbGkTUnmXxf9B4D7Vt2HM8A+DAVVhUghibWqgVzVZfUs/OMm3nzgeyqK6ph0e1+6hlo5Vm/HFaCT6okDR3EIPS/bcrl5xBXeF+g9CfKKm469IynJqQYBSX29tRZnvYIwtj4uu6rgGMWuIJITfedd2Dr3JdjopCgzo8lxo15H7JgJuIJCKayuZq/Tga1OIdaq+mZ63HYTNpeddW8uRFEUNgcVMLI2kejYRvPa8u/mUhIsuarrzCb3jrjhBgAqvv6f33lLTwtPoSXQnTGuHtIRRcL7G45SsX5+w/H4i29p2w3Tv1Mj+7pPbN24ic+p0VGL74a6ct/XGNpfUOiEFUWcmunJnlEBbonZk6R7LnHBCYqOPSOYes8A5rwwimseS2PWE8MwnKDmje05ir+M+QvpZenM3zu/mTs1cqxItcPGh6gahZRQ4mlWNOrqbnToHkGK1YxLQo69+WYpDpeTB1d8wSZzJx4y5XHj8Cm+O4cdr1ejOOkyQHWgH9xU4POernqJvg0mz6NbvgMgud9w3xcIQUyEicIi76qZJfsPUNe5BzvySyk3C3qfkIHbecxFFEcnYvtuCQd2fk+1RTIovmly46IDnxFaJ5g08c4mx8OuUIs01m36ye+8Zb26s9OZz37DlwuFlOggJvWN590fM4moPsQBfTcOzFhCUtcAzUYn4qhRQ2Iju0KXVoTSAphscNVcqMqHrx/2eUmDRtGOpie9zoqiO7X72Q+WgUGHObn1vsTTzQUnKI4jhCAuOZTojt7mickpkxnfaTxzd86lpK7Ex+imZJephck6hCbgtLtZ8HijzT51vBoum+TZTWfWNS8onl+zmA90SYy1Z3CDD79AA55SybgdRHYIIqFrGLtWZVNf460FKU6BweJffXe73Xz00Ud88skn5OY2luzKPHwAEw7i+/v31+j1eqrrm2pJR9MPIj29Hj49Wgg6wZyujVqJEALjjJl0K85k6af/BmDIoMaeyblH97IxvJhJsi8mS9MwVn1ICLrgYOxHjvidk+JpUSrM55ZD8OfO/RO7Y7fX0ct9kKLo4fQcdLGaGLrmL3B0Xcs3kBIW3QWvXQSlh2HKX8DQBmGfOATGPgq7Pobdn3mdFkaPr65dBYXtlAVFfXo55pTQc84/ARewoGiJ+4fcj91t529b/9bitfkVqqDoGNWBHz462HD8zn9d0uAvSLGqL62MOv8fpo83LuENXXeut+9j4eVXYrE2s7NoEBSqYLhoZldqKux88bdt3kl3Lj1mm/8PX3p6Ovv27WPPnj28/fbblJWVgZRkljlJCnahN/gvtVBQUk9SXNM/5g1rVjV8H1WQgdVRz/Skpj6OwXf8khqTlWXhe4mo1dGrZ2P1+Y+/exW3XjD7knt9PtPcsyeyrg5nvm8NSnq0NmHSBMWZpFd8KM/Gq9FscaEes99Xv4VVz6vF++ZPb2oO+vJeeLU/uD2+u7wdsOMDNdJp9gfQbQJt5uKHIDENljwAFdlNTh0vM+52tU8JDwCd3orUO1DcgfshT8RVYcdVWIul+7lZeVgTFH5ICUvhtn63sfjQYjbkbWj22vzqQnSKHspN7Fufh84guOOfYxvKhQDEm41YdIJMP4LiWM5+7q9JoG99Nk+Pntxy4xydxxzlScTr0C2cyXf0p7rUzqJXtlJbqR53OJwIxYA5yP/LvqJCDa/91a9+hRCClStXUnV0O8VKGMmdmk+OMhkFbqXpXLNz8zC4nUSNGkt8ZSlX7PiRl7cdbTouNIQjs64lK0ZQZlPQCfVn5Xa7+Mr+E6llIXTv7bvMQ/AllwBQvmiRz/PSof6MdRZNUJxppk1UfQrd09+C7C2w70vo7wl5zlgDLybBX7rAc7GwdQFUHAOdZxNT7vmMXPch9Jx8ahPRG1QTlKLARzep2ooHnefb8qodVFbuPLXneDDoVc3XVd824WNPV9PLLD00QXHecceAOwgxhbD40OJmryusLyDIEca2pVkg4cZnRzTxewDohKCzxexXUCw+uBNF6Lk/IYjQ4PA2zTc5NZorHxqEo97Nhi/UelXHy3dYgv1XazkeDaXT6Rg6dCi7du1i7xa1eF9yX5/9qBoIDzFSXN7UnFalgEtv5N6J4+hw6SQia6vYsHtjQ17FcaY+dE/D93N/UpP516yaR2Gwm5kd/ZvdwmaqDu7q733nUygeH4VmejrzBPe9HH6zGayR8Jan18SQW+C6hRDsCX2uLWlq9pGeQIuM79UNUIT/routIqorXP485G6FQ8thw+vw9SMITw8LxRLCtu1zqKzyjqCrq8vh8OFXyMx8Haezwuv8yeg9DnKXvW0VZOvTy9GFGDHEnZsVAzRB0QxmvZnRiaPZnL+52etKHMUEOcIoya5mws29CYn0XWQuxWYiw4+PYlWdgS6OQi7vF1gZ7YaIDUPTZ0V1CKbH0DjSNxfitLupLFN3OLYQ3y/NnJwcVqxYQVRUFLGxsaSmqqGo3+xSk5biew1tdhodu3WlvF5P1TG1aZOUkpQjRxi6aRO5mzZx++jhHIpJJO3oAZ546+MmY4MsISyb+QMGV0f+uftJVh7Zzid7FhJSB9Om/NbvM43RUQibDftB7/4fcILpSRMUZ4fo7mrJjR6TIDQREgerGkKaJwIqPrXp9YvvUov6bV2gFv6ztKMzt9/VENoR3r9Gzdre9Aa8qQqwzml/xWAIYdu2X1JV1dh0LD//CzZumkLm0dc5fORltmydhdPpVVCiAUVxUOBW86/KluzHmV8TcIc66VKQbgV7ehmW7hGt7jt+ptAERQsYdUbq3fW4Fbffa2qMFQQ5wkmbkkzPi/z3/022mDlWZ0fx8SHK19noLyswGQJsY+7pCYHB+2XYa0QCLrubI9uLGjSKEB9d9KSUfP755+j1em688UYMBgMJCQnMmjWLPhFORht2oW9hPvGpowAo2aH6JSqPZjBs0090OZJB7qLFCCF469e3UBEchznvAFsPNDVBJYSG886k/4Bi4YE1t7M2vICJrh5YbM0ny5l7dEfW1FC3axf1B5o2mDpuehImLerprBHbC67/CB7Yo9YlAxh+B8x8A674W9Nrd34Erw1XNYuLH2rfeZiD4Y41au+KsY/B6AcbSt9YOo5h8KD30OttbNt+E4WFS9m1+1727H2Q4OCejByxmkEDF1Bbe4QjGf/w+4jDR16hTskk3jEL5aCJgr9tJe/PmyhbfAjF7v+9UbUmm9xnN5DzxI8ota5z1uwEmqBokTEdx1Bhr+CO7+6guM67NMB3R78j35nLhPHDGD69+f4SyTYzdYokz+4dmVSkDyXW0IpCYMeLBPqIe03oGkZotIUDG/Ia6jyF+hAUGRkZlJSUMG7cuCbtO3v37s21SWVMCGq5w581WhWMteVqgcDSDz5sOOfcuJH1t9xC0Y8/MnLQEBQJHy380Gu3NSgxiYcGPouiU4XfL0bd0eJzQydeBkDmL64lY8aVlH/xReNJz/3P1d3ZBcWJvwNrhFq3KXEI/GYLPFUGDx1UQ2Dtlao24WnE1a4ERau9K8b9H1z6FIz6LVz6BzCYsVo7M3jQ++j1Nnbtvpvi4uV06fIggwd9gNXakcjIUSQk/IKcnA+pqvIudllcvIpjx94iMfFG+k76Ex0eGU74Vd0wJ4VSszGP4nl7UGqduMrqkc5GoVGfXkbFNxmYU0IJGhpP8JhErP29k1rPFQLcvl64XJZ0GU9e9CTPbXiOBXsW8GDagw3nvs/+nke+f4TU6FRu7Xdri/dKDVZ3Vjuqakm0NO52i8oLqDbYiGlNR1hXPQi96rQ7CaET9Bgez+avM3F7cgnCo73V+R9++IHg4GAGDBjgfX9HTeNOEFCkgkBQkruFpTveZMLQ+4iL6UvRHtXRb4tUP+Smzp0bChmE5+ZCbi6lW7dy2ZdfcvBIH3Q5ezmcW0S3k5L45gwez4G3FQrCofvFfspJn0DEdbMpX7wIpaYWV24upfPnEz7D0+va4xyVyrnV/EXjBKI9v+OQOLhpkerIDm19r5RWIwRMfLbJIZstieHD/kdZ2XqCg/titTadR5eU+ykuXsHmLVfTseMcOibehNWaiNtdy959jxAc3Jvu3R4HQB9qInhYAsHDEqjdXkjpxwfJfW4DSNDZDETN6Ys5KZTq9Xnogo1E3dSnsU/2Ocy5P8OzjBCCa3teS2p0KjuLGyMkNuRt4IFVD9Ajogf/nvDvhsZFzdEvxIpRCLZWNm3BuHSPGmN+UaeW+2c34HY0hsj6oOfweJCQv1vVPCJjmgqK7OxsMjIyGDlyJEajj4goZ12DoDiQ/j/GzUvl8nmpTF82hxeK1vGrJddRV12IyarGpBuD1YzwuGuvBcARHITu0Uew/vUVTHYHWf/7msqCLAAMPnp1b8sq52vzkzz0pZ7S+QtaXL7OZqPrV1/RfeUK9NHR2Pftx1WsanzC4Lm/27/ar3EOIQREJDcmkZ4FDIYQYmIu8xISAGZzDMOGfkls7BSOHXuLdevHsH7D5Wzb9kuczlJ69ngavQ/N3jYwlpg7UwkenUj4jK7orAaK39lN3f5S6veXYBsSd14ICdAERcD0je7L3pK9uBU3Wwq2cN/K+0gKS+KNCW8QYgqs+JxZpyPZavLKpfihrIp4exFDa1qupd+AlI1hhT4Ij7UR3yUMxQVSuDFbmgqVjRs3YjabGTLET5Xc9KVqXDvw302vUCkgQoGOOiuPdphApk7y5rJ7iO6jZm2XHFKFaE2WKgzE5RPpecstSE8ZDYPZRHSnrgC4fLzAF246hjMknJDp06lYvBhnXl7AP4qo224FKSme+6Z6wBPFJTVBodFOmM2x9O3zCiMuWk73br/HYkmgqnovsTGTCQ9P8z+ucyjhU7sQPKID0benogsyUjJvDygQNKj1LYnPFpqgCJB+0f2oc9Xx5eEvuWfFPcQHxTN34lzCLeGtuk8ni4ljJ0Q+HSvK5ktbPwZV7UcsvgPq/EdXNEEqQPM2+G5Djn8Qm15XX1/P3r17GTBgAGY/kUHVQrDLZGLyO/35wlVEqj6Ej27bxcdzNnPjxFcZjZUVFQcJSRmATiiU5alJTfZS1VdhDFU1mJKNqmkqfuRILh46EICv12xs8qyqeidf7cjjitQOJNyj9izIf/75gE1H4VddpT77oOpTaYh68qUpaWicAjZbMp0738qggfO4ZOwe+vf37uLnD0O4mdg7B2DsEIQpORRjfNsaiZ0NNEERIP2i1H4MT617ighzBG9OfJNoa+udT52tZo7VNwqKL7YtRwodd2UtBCS82s9n2QEvzCHgrAFHrd9LjBZV4xAGhZqaGhTPizc7Oxu3202vXv7r8DzUMZnrE+PJ1kOqNDK7e9PWod2sceQIN0dXfoQidQSHq85wW0e1ZImzSC24aN+1C4fFQlivXgzvnUS1KZKCA9t5f9nGhvl8tSOPOqeb2cM6YUxMJOb++6levoKCP78QUJihPiwMhMBVqD7zuDbiqxWqhkZ70ZZgCX2oidh7BxFze//TMKPTRyA9sy3A94DZc/2nUso/CCEGAv8BLIALuFtKucnH+HDgLaAfIIFbpZTrhRAvAVcADuAwcIuUsrwd1nRaSA5LJsQUQrAxmLcvf5u4oMB6Xp9MZ4uJCpebCqeLENx8XWeiozuPYbP+CUIHc8fCp7dCUAykjPF/ow6DVa3i2Dq/pQ6O9wd3KQ5eeuklDAYD8fHx2O12hBB06OC/idKR4AioL+ZiEcRrv1zv9UcRYo3CXpfJvK9eI1IXTOpsNe8hKD4Bp17X8LI25+ZRExfXMP6OX87irbffJX3dN/y9qIgHbpjGwp+O0TMuhIGeBjiRN8/BVVBA6bx56MPCiPnNPbSEMJlwezLMnXm56MPCfLZC1dA42wghQH9+ReQFolHYgfFSygGoPa8nCSEuAv4CPCOlHAg85fm/L/4OfCul7AUMAI5ntnwH9JNSpgIHgf9r6yLOBDqh482Jb/L+lPfpENy6LnUn0tnjK8iqd/Dp+kVsC+3D7zLnqT1/OwyEGz8HnREWzIB3p8KuT33fqMslavbrFv8Vbs1Wzz5ASCZOnEhaWho6nY7q6mouvfRSLBbfiYEAeoOZqV2m8u9fbvC5czJ7atroy4LRhdZijFbDGqXDgcGtoHjuLQ0GhKKQl5fHwYMH6dohhuce/x111lhKDm5l+Y6j7MyuYPawTg3PEUIQ+8jDhM2cSfG//kXlty33ydaFhKB4+lC4CoswxLVNkGtoaHjTokYhVd3/eF660fMlPV/HQ2nCgNyTxwohQoExwM2eezlQNQiklMtOuHQDcM3J4881+kb3PeV7dD6hiuxL9TEMqNvPtV26Ncabd7sUHs1Qyyxvew+OroWyTDUR6cQXttECA6+Hjf+B6iII9u7VbfHUd+rWJ4lRo1K9zvtDSkmlo5Jgo//GP31jB2Is347dqFBqDKe6tITty74m+4fVDACsnToBYKitwY7gjTfeAGD48OFceuml/GLGFJYsnMcrH6/AZEhg5qCm0SZCpyPhuWepP7CfguefJ2jUSPQh/oMGTEmdqduyFUdODu7ycvTh4QGvV0NDo3kC8lEIIfRCiO1AIfCdlHIjcD/wkhAiC3gZ3xpBF6AIeFcIsU0I8ZYQwpc94FbgmzbM/7yjm82CXsCq/DyyjFFMKfkBXVinpheZQ2DqK/BYFvS/FlY+B/OvgGMbIWdLY4GzQTeB4lLLKfsgNiWUETO7MuqalvMSTiSrKosqRxUpYf5r7qSN/B1bfrkTS1gYhmqFD596mE1ffILDoMdpNGDevouq1auxlJZRb7EwduxYBg8ezMaNG/nTn/7E5jWqlmAWLib3iyfc5h3qKwwGEp55FldJCQUvvNDsnEOnqn0qSv/7HjqrtaHek4aGxqkTkKCQUro9JqaOwDAhRD/gLuABKWUn4AHgbR9DDcBg4HUp5SCgBnjsxAuEEL9H9XG87+vZQohfCyE2CyE2F3kcpOczNr2OnjYLO2pV002wq8Z/Fy+DSS15MO1VyN8J71ym1ql553LI362WSYju6bfto16vY/DlSYTHtq7Q2CcHPwFg1bFVzV5XV1ONObcWcx1Ul5Zw3bMv8cu/zyX5xRdx7d9P9p13IWw2LlnyFePGjWPatGlMmzaNoUOHUuz5XQoks4d29vsMa/9+RN1+OxWffU7BSy+hOHzXygq/Wo18ql6xAn1YGO7y8latWUNDwz+tinryOJtXA5OAOcDnnlOfAL7KjGYD2R4NBOBTVMEBgBBiDjANuEH6CW+RUs6VUqZJKdNiYrzNK+cjqSE2dtepMf4m6VKrXPpDp4O0W9WSB1f8Haa8DCWHVaf38mcgrg/k72rX+U3rou7ON+Zv5P5V9/Pe3vcoq/cO29Xp9eikag4bduW1JHTvCUDolCnEP/0HAMKmX4HBZvMsRUdaWhpTp07lmmtUS2MnXTkXdYlsdj4x991L+KxZlL79DhlXXUXd9u3eczGbMcTG4szORthsKNVtq+KpoaHhTYuCQggR44lcQghhBSYA+1F9Esdbn40HvEp5SinzgSwhRE/PoUuBvZ57TQIeBaZLKf3HeP4MGRDauMPvbgow+iE4BobcDMNuh9/8BKmzYO1fYc8iqMqDeu92pG2lZ2RPNl6/kZv63MS+kn28+NOL3PTNTV61rt5NX8DhDjW4O4dx0VXXNjkXMXs2PXfuIP73v/f5jF69ejFk9KX8YuYVLYYZCr2ehGeeptPcN1Bqasm87nryn/sjrrKmwivo4otBSmo2rEdnOzfLNWtonI+IluLUhRCpwHxAjypYPpZSPiuEGI0a0WQA6lHDY7cIIToAb0kpp3jGD0QNjzUBR1DDYMuEEIdQQ26P9xrdIKVs2iD5JNLS0uTmzc2X/D4f2FpZw5Qtqlw9FHmI4AFt9OMfWQ0rnlWLrE16sSEjub3ZlLeJ36z8DUHGIOJt8URaI4mxxvBZ+mdM7zqdp0c8jfEMlV9wV1dT9NdXKVu4EF1ICDH33UvEddchdDrq9uwl8+qrAQiZNImOf3v1jMxJQ+NcRwixRUrpP4W8pfGB1k0/F/i5CIp6t0Ly9ztJcZWyfsK4ptFM5yh7ivc0tIU9XH6YoroipnedzrMjn0XfTCmR00X9gYMUvvgCNevWEzR6NIl/e5W6LVvIukPda3R+9x2CRoxo4S4aGhcGmqA4T1lfXk0Xq5k48/lXZkKRCjXOmoBrXJ0upJSUf/QR+X98HlxqcIAhLpaQyy7za/LS0LgQOVVBoZUZP0uMCPefo3CuoxO6sy4kQE3Mi5g9G6Qk/5lnETYbXb/9Fp3V2vJgDQ2NgNEEhcZ5T/isWbjKygi55BJNSGhonAY0QaFx3iN0OmLuvvtsT0ND42eLVj1WQ0NDQ6NZNEGhoaGhodEsmqDQ0NDQ0GgWTVBoaGhoaDSLJig0NDQ0NJpFExQaGhoaGs2iCQoNDQ0NjWbRBIWGhoaGRrOcV7WehBBFwNHT/JhooLjFq84PtLWce/xc1gE/n7X8XNYB/teSJKVsc0Of80pQnAmEEJtPpXjWuYS2lnOPn8s64Oezlp/LOuD0rUUzPWloaGhoNIsmKDQ0NDQ0mkUTFN7MPdsTaEe0tZx7/FzWAT+ftfxc1gGnaS2aj0JDQ0NDo1k0jUJDQ0NDo1k0QaGhoaGh0SwXpKAQQnwkhNju+coUQmz3HL/hhOPbhRCKEGKgj/G/EELs8Zw/q2F17bCWSCHEd0KIdM+/EWd6DZ55+FyH51yqEGK952e+Swhh8TF+gOeaXUKIr4QQoWd0AU3ncqprGSiE2OAZv1kIMeyMLqBxHqe6Dr/jzzSnuhbPdfcKIQ54rvvLGZu89zxO9ffytBAi54R7TGnxoVLKC/oLeAV4ysfx/sARP2N6Az2B1UDa2V7DKa7lL8Bjnu8fA148l9aB2oVxJzDA8/8oQO9jzE/AWM/3twLPne11nMJalgGTPd9PAVafj+vwN/5sf7XxdzIOWA6YPf+PPdvrOIW1PA38rjXPuaBboQohBHAtMN7H6euAD32Nk1Lu84w/fZNrJW1dCzADuMTz/XxU4fdoO08vYHys4zJgp5RyB4CUssTP0J7A957vvwOWAk+exqm2yCmsRQLHNaIwIPd0zrMlTmEd/safNU5hLXcBL0gp7Z7rCk/3XFviVH8vreGCND2dwMVAgZQy3ce5Wfh/uZ6LtHUtcVLKPADPv7GnaX6BcvI6egBSCLFUCLFVCPGIn3G7geme738BdDrN8wyEtq7lfuAlIUQW8DLwf6d/qs3S1nX4G382aetaegAXCyE2CiHWCCGGnpHZNs+p/F5+I4TYKYR4JxBz889WoxBCLAfifZz6vZTyC8/3PnfaQojhQK2UcvdpnGLA/FzW0sZ1GIDRwFCgFlghhNgipVxx0j1uBf4hhHgK+BJwtOvkT+I0r+Uu4AEp5WdCiGuBt4EJ7boAD6d5HcdpTqNtN07zWgxABHCR59qPhRBdpMeW096c5rW8DjyHqrk+h2q+urXZCZ1tG9tZtO0ZgAKgo49zrwKPB3CP1ZwDPopTWQtwAEjwfJ8AHDiX1gHMBuad8P8ngYdbuE8PYNO59jsJdC1ABY05TgKoPB/X4W/8+bgW4FvgkhP+fxiIOR/XctJ9koHdLT3vQjY9TQD2SymzTzwohNChmi4WnpVZtY1TWcuXwBzP93OAL5q59nTjax1LgVQhhE0IYQDGAntPHiiEiPX8qwOeAP5zBubbHG1eC6pPYqzn+/HA2TTZnMo6/I0/W5zKWhbj8QUIIXoAJs5uxdlT+VtJOOG/M1HNts1ztqX8WZTI84A7fRy/BNjg4/hbeLQHzw83G7CjSvWl5/FaooAVqC+jFUDkObiOG4E9ng/0X/ys47fAQc/XC3h25OfpWkYDW4AdwEZgyPm4jubGn29rQRUM73mu2QqMP4/X8l9gF2qE1Jd4LArNfWklPDQ0NDQ0muVCNj1paGhoaASAJig0NDQ0NJpFExQaGhoaGs2iCQoNDQ0NjWbRBIWGhoaGRrNogkJDQ0NDo1k0QaGhoaGh0Sz/D9La52C55eEvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7868852459016393 122\n",
      "100 0.7411764705882353 170\n",
      "200 0.6949152542372882 177\n",
      "300 0.7324561403508771 228\n",
      "400 0.0 0\n",
      "500 0.7527386541471048 1278\n",
      "600 0.6351209253417456 951\n",
      "700 0.6090594345066359 3466\n",
      "800 0.6122448979591837 49\n",
      "900 0.7423728813559322 2065\n",
      "1000 0.8421052631578947 57\n",
      "1100 0.8958333333333334 96\n",
      "1200 0.7142857142857143 112\n",
      "1300 0.4627027027027027 2775\n",
      "1400 0.7962962962962963 54\n",
      "1500 0.65234375 256\n",
      "1600 0.8786610878661087 239\n",
      "1700 0.75 620\n",
      "1800 0.9081632653061225 392\n",
      "1900 0.6695652173913044 115\n",
      "2000 0.5 30\n",
      "2100 0.6511627906976745 43\n",
      "2200 0.6519083969465649 2620\n",
      "2300 0.8080808080808081 99\n",
      "2400 0.732824427480916 131\n",
      "2500 0.6774193548387096 279\n",
      "2600 0.7200282087447109 1418\n",
      "2700 0.3444794952681388 1585\n",
      "2800 0.3269330565646082 1927\n",
      "2900 0.5006321112515802 791\n",
      "3000 0.761485826001955 1023\n",
      "3100 0.0 0\n",
      "3200 0.5285565939771547 963\n",
      "3300 0.41008403361344536 1190\n",
      "3400 0.19899244332493704 1588\n",
      "3500 0.1617391304347826 2300\n",
      "3600 0.07174490699734277 2258\n",
      "3700 0.1702127659574468 235\n",
      "3800 0.3938294010889292 1653\n",
      "3900 0.4829244829244829 2079\n",
      "4000 0.0 0\n",
      "4100 0.6861538461538461 650\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 26.327870508506834 21.494230998857958\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  5706481 3201096 0.46360486351433333\n",
      "compare to Census 5706494 Leip has Trump + Biden = 3201142.0 0.46360486351433333\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 5706494\n",
    "atlasTrump = 1484065.\n",
    "atlasBiden = 1717077.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 5706481 5706481.0\n",
      "state Trumpers before, after slice opn are 1484043 1484043.0\n",
      "state Bideners before, after slice opn are 1717053 1717053.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 90 grids for 8 districts\n",
      "starting grid no 0 at x = -96.96423138888888, y = 43.77441565000001 \n",
      "starting grid no 5 at x = -96.96423138888888, y = 46.52496215000001 \n",
      "starting grid no 10 at x = -96.41450816666666, y = 43.774415650000016 \n",
      "starting grid no 15 at x = -96.41450816666666, y = 46.52496215 \n",
      "starting grid no 20 at x = -95.86478494444444, y = 43.77441565000001 \n",
      "starting grid no 25 at x = -95.86478494444444, y = 46.52496215000001 \n",
      "starting grid no 30 at x = -95.31506172222224, y = 43.77441565000001 \n",
      "starting grid no 35 at x = -95.31506172222224, y = 46.52496215000001 \n",
      "starting grid no 40 at x = -94.76533850000001, y = 43.774415650000016 \n",
      "starting grid no 45 at x = -94.76533850000001, y = 46.52496215 \n",
      "starting grid no 50 at x = -94.21561527777777, y = 43.77441565000001 \n",
      "starting grid no 55 at x = -94.21561527777777, y = 46.52496215000001 \n",
      "starting grid no 60 at x = -93.66589205555555, y = 43.77441565000001 \n",
      "starting grid no 65 at x = -93.66589205555555, y = 46.52496215000001 \n",
      "starting grid no 70 at x = -93.11616883333335, y = 43.774415650000016 \n",
      "starting grid no 75 at x = -93.11616883333335, y = 46.52496215 \n",
      "starting grid no 80 at x = -92.56644561111112, y = 43.77441565000001 \n",
      "starting grid no 85 at x = -92.56644561111112, y = 46.52496215000001 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 222001.74049976532 7106496.507869088 85.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 8   #MN congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f5a471b-867e-4db1-abdc-55f4b3d1f27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 0.00016990724566590647\n"
     ]
    }
   ],
   "source": [
    "#print(p,vtdGeom[p].area)  #this will spit out the precinct number where we choked (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b538ed92-bf09-41ce-9758-8a764cbff950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigh, this precinct's geom is still whacked.  Convex-hull it\n",
    "# vtdGeom[2034] = vtdGeom[2034].convex_hull  #after this line, go back up and redo the grid code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9950574466045492e-07 2.451117150001066e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 7 8.0 0 113.7 1.9244 203162.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 7 0 203162.10331759925 1.8501\n",
      "we have 2 non-opposing shorted wedges for tract no 9\n",
      "we have 2 non-opposing shorted wedges for tract no 10\n",
      "I am working on tract number 20 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 21\n",
      "we have 2 non-opposing shorted wedges for tract no 22\n",
      "7 yoyos for tract,wedge,wedgePop,r= 29 0 194122.26980624 2.5255\n",
      "8 yoyos for tract,wedge,wedgePop,r= 29 0 61765.37182525956 1.2627\n",
      "9 yoyos for tract,wedge,wedgePop,r= 29 0 195790.57737483928 2.6017\n",
      "10 yoyos for tract,wedge,wedgePop,r= 29 0 82042.0698074238 1.9322\n",
      "10 yoyos for tract,wedge,wedgePop,r= 29 0 107985.36381471544 2.2669\n",
      "10 yoyos for tract,wedge,wedgePop,r= 29 0 151168.17202359805 2.4343\n",
      "11 yoyos for tract,wedge,wedgePop,r= 29 0 193424.0755198183 2.518\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 31 3.0 1 58.8 1.5024 264523.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 31 4.0 1 58.8 1.2547 264523.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 31 5.0 1 58.8 1.0479 264523.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 3.0 1 55.5 1.2841 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 4.0 1 55.5 1.2027 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 5.0 1 55.5 1.1265 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 6.0 1 55.5 1.0552 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 7.0 1 55.5 0.9883 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 8.0 1 55.5 0.9257 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 9.0 1 55.5 0.867 231076.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 34 10.0 1 55.5 0.8121 231076.4\n",
      "I am working on tract number 40 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 49 4.0 1 36.7 1.9712 295424.5\n",
      "we have 2 non-opposing shorted wedges for tract no 52\n",
      "we have 2 non-opposing shorted wedges for tract no 53\n",
      "we have 2 non-opposing shorted wedges for tract no 55\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 9.0 0 100.2 1.3113 187007.1\n",
      "I am working on tract number 60 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 67 3 61190.50227355509 0.6504\n",
      "8 yoyos for tract,wedge,wedgePop,r= 67 3 315097.12064598245 2.1736\n",
      "8 yoyos for tract,wedge,wedgePop,r= 67 3 296154.4283620369 1.412\n",
      "9 yoyos for tract,wedge,wedgePop,r= 67 3 113551.35406095031 1.0312\n",
      "10 yoyos for tract,wedge,wedgePop,r= 67 3 261388.8818688244 1.2216\n",
      "11 yoyos for tract,wedge,wedgePop,r= 67 3 139041.41082455945 1.1264\n",
      "11 yoyos for tract,wedge,wedgePop,r= 67 3 179174.04611540394 1.174\n",
      "12 yoyos for tract,wedge,wedgePop,r= 67 3 241897.5523175681 1.1978\n",
      "12 yoyos for tract,wedge,wedgePop,r= 67 3 208768.32436640214 1.1859\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 68 7.0 0 125.4 3.261 183163.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 70 3 286106.6681610666 1.1838\n",
      "8 yoyos for tract,wedge,wedgePop,r= 70 3 76229.19946671021 0.5919\n",
      "9 yoyos for tract,wedge,wedgePop,r= 70 3 286483.069298902 1.1849\n",
      "10 yoyos for tract,wedge,wedgePop,r= 70 3 100092.93937076995 0.8884\n",
      "10 yoyos for tract,wedge,wedgePop,r= 70 3 151314.82749089674 1.0366\n",
      "11 yoyos for tract,wedge,wedgePop,r= 70 3 263186.4922131966 1.1107\n",
      "11 yoyos for tract,wedge,wedgePop,r= 70 3 250482.85063194094 1.0737\n",
      "12 yoyos for tract,wedge,wedgePop,r= 70 3 189802.99006005592 1.0552\n",
      "13 yoyos for tract,wedge,wedgePop,r= 70 3 221459.99096193683 1.0644\n",
      "7 yoyos for tract,wedge,wedgePop,r= 72 3 307732.329542188 1.2259\n",
      "8 yoyos for tract,wedge,wedgePop,r= 72 3 76917.73082861258 0.6129\n",
      "9 yoyos for tract,wedge,wedgePop,r= 72 3 279013.2630809154 1.1582\n",
      "10 yoyos for tract,wedge,wedgePop,r= 72 3 98426.30950728731 0.8856\n",
      "10 yoyos for tract,wedge,wedgePop,r= 72 3 143967.54909230518 1.0219\n",
      "11 yoyos for tract,wedge,wedgePop,r= 72 3 261318.16723825363 1.09\n",
      "12 yoyos for tract,wedge,wedgePop,r= 72 3 197682.8126117802 1.0559\n",
      "13 yoyos for tract,wedge,wedgePop,r= 72 3 253032.92693300088 1.073\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 77 6.0 0 142.5 3.7077 192974.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 77 3 208741.9647048455 1.1698\n",
      "8 yoyos for tract,wedge,wedgePop,r= 77 3 64719.55749205063 0.5849\n",
      "9 yoyos for tract,wedge,wedgePop,r= 77 3 209205.45950583968 1.1751\n",
      "10 yoyos for tract,wedge,wedgePop,r= 77 3 79809.63527165951 0.88\n",
      "10 yoyos for tract,wedge,wedgePop,r= 77 3 122781.12425705834 1.0276\n",
      "11 yoyos for tract,wedge,wedgePop,r= 77 3 202610.46832868975 1.1013\n",
      "11 yoyos for tract,wedge,wedgePop,r= 77 3 193247.03434619814 1.0644\n",
      "12 yoyos for tract,wedge,wedgePop,r= 77 3 154210.17764768735 1.046\n",
      "I am working on tract number 80 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 92\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 93 2.0 0 90.0 1.593 180789.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 93 3.0 0 90.0 1.5767 180789.2\n",
      "I am working on tract number 100 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 119 5.0 1 90.0 3.664 199142.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 119 6.0 1 90.0 3.3689 199142.3\n",
      "I am working on tract number 120 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 136\n",
      "I am working on tract number 140 of 1505 tracts\n",
      "I am working on tract number 160 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 162 0 281827.2881586989 2.6039\n",
      "8 yoyos for tract,wedge,wedgePop,r= 162 0 91610.56345244296 1.3019\n",
      "9 yoyos for tract,wedge,wedgePop,r= 162 0 281740.9011156571 2.6032\n",
      "10 yoyos for tract,wedge,wedgePop,r= 162 0 141067.09394373777 1.9526\n",
      "11 yoyos for tract,wedge,wedgePop,r= 162 0 253232.02346048 2.2779\n",
      "11 yoyos for tract,wedge,wedgePop,r= 162 0 235167.06329613584 2.1152\n",
      "11 yoyos for tract,wedge,wedgePop,r= 162 0 185301.72174803534 2.0339\n",
      "12 yoyos for tract,wedge,wedgePop,r= 162 0 157727.78330044379 1.9933\n",
      "12 yoyos for tract,wedge,wedgePop,r= 162 0 169172.44082445072 2.0136\n",
      "I am working on tract number 180 of 1505 tracts\n",
      "I am working on tract number 200 of 1505 tracts\n",
      "I am working on tract number 220 of 1505 tracts\n",
      "I am working on tract number 240 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 247\n",
      "I am working on tract number 260 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 3.0 2 90.0 2.3353 237846.6\n",
      "I am working on tract number 280 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 298 1 241053.32885164127 2.8968\n",
      "8 yoyos for tract,wedge,wedgePop,r= 298 1 109877.25882628147 1.4484\n",
      "9 yoyos for tract,wedge,wedgePop,r= 298 1 243093.65302326425 2.9642\n",
      "10 yoyos for tract,wedge,wedgePop,r= 298 1 147383.03197937034 2.2063\n",
      "10 yoyos for tract,wedge,wedgePop,r= 298 1 189120.85461769585 2.5852\n",
      "11 yoyos for tract,wedge,wedgePop,r= 298 1 237037.76107583518 2.7747\n",
      "11 yoyos for tract,wedge,wedgePop,r= 298 1 232436.79429595082 2.6799\n",
      "I am working on tract number 300 of 1505 tracts\n",
      "I am working on tract number 320 of 1505 tracts\n",
      "I am working on tract number 340 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 344\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 345 3.0 3 45.1 1.603 191033.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 3.0 3 88.4 1.5529 208860.4\n",
      "I am working on tract number 360 of 1505 tracts\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_454/99954720.py:66: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
      "/tmp/ipykernel_454/99954720.py:99: RuntimeWarning: invalid value encountered in multiply\n",
      "  wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 380 of 1505 tracts\n",
      "I am working on tract number 400 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 414\n",
      "I am working on tract number 420 of 1505 tracts\n",
      "I am working on tract number 440 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 442 2.0 0 90.0 1.0578 203877.8\n",
      "we have 2 non-opposing shorted wedges for tract no 443\n",
      "we have 2 non-opposing shorted wedges for tract no 449\n",
      "we have 2 non-opposing shorted wedges for tract no 454\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 454 17.0 2 78.8 5.1234 328184.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 454 2 328184.6541273791 5.6242\n",
      "I am working on tract number 460 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 461\n",
      "we have 2 non-opposing shorted wedges for tract no 473\n",
      "I am working on tract number 480 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 497 5.0 1 37.5 2.1628 365271.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 497 1 365271.03405566415 2.2742\n",
      "I am working on tract number 500 of 1505 tracts\n",
      "I am working on tract number 520 of 1505 tracts\n",
      "I am working on tract number 540 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 557 5.0 0 90.0 2.6023 191968.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 10.0 0 133.0 1.6333 185674.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 11.0 0 133.0 1.5844 185674.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 12.0 0 133.0 1.5369 185674.8\n",
      "I am working on tract number 560 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 562 0 234431.7274700139 2.5992\n",
      "8 yoyos for tract,wedge,wedgePop,r= 562 0 108237.3828518945 1.2996\n",
      "9 yoyos for tract,wedge,wedgePop,r= 562 0 234435.12058923405 2.601\n",
      "10 yoyos for tract,wedge,wedgePop,r= 562 0 138137.64985551743 1.9503\n",
      "11 yoyos for tract,wedge,wedgePop,r= 562 0 230222.13179281435 2.2757\n",
      "12 yoyos for tract,wedge,wedgePop,r= 562 0 162160.3473391062 2.113\n",
      "13 yoyos for tract,wedge,wedgePop,r= 562 0 183545.47211284138 2.1943\n",
      "14 yoyos for tract,wedge,wedgePop,r= 562 0 170102.37192060583 2.1537\n",
      "7 yoyos for tract,wedge,wedgePop,r= 568 0 224890.25383811147 3.4076\n",
      "8 yoyos for tract,wedge,wedgePop,r= 568 0 49615.82689181861 1.7038\n",
      "9 yoyos for tract,wedge,wedgePop,r= 568 0 224530.52433902147 3.2555\n",
      "10 yoyos for tract,wedge,wedgePop,r= 568 0 96563.94331556102 2.4796\n",
      "11 yoyos for tract,wedge,wedgePop,r= 568 0 200199.94278377388 2.8675\n",
      "12 yoyos for tract,wedge,wedgePop,r= 568 0 131684.39142377354 2.6736\n",
      "12 yoyos for tract,wedge,wedgePop,r= 568 0 161248.3921456223 2.7706\n",
      "we have 2 non-opposing shorted wedges for tract no 572\n",
      "I am working on tract number 580 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 591\n",
      "we have 2 non-opposing shorted wedges for tract no 593\n",
      "I am working on tract number 600 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 605 1 193972.77559318615 1.9658\n",
      "8 yoyos for tract,wedge,wedgePop,r= 605 1 47864.09644459939 0.9829\n",
      "9 yoyos for tract,wedge,wedgePop,r= 605 1 193970.33573937826 1.965\n",
      "10 yoyos for tract,wedge,wedgePop,r= 605 1 93918.73591987588 1.474\n",
      "10 yoyos for tract,wedge,wedgePop,r= 605 1 130994.1698881218 1.7195\n",
      "11 yoyos for tract,wedge,wedgePop,r= 605 1 190970.1987517089 1.8422\n",
      "12 yoyos for tract,wedge,wedgePop,r= 605 1 172482.94315495636 1.7809\n",
      "13 yoyos for tract,wedge,wedgePop,r= 605 1 189631.54829261056 1.8116\n",
      "13 yoyos for tract,wedge,wedgePop,r= 605 1 184910.3093840705 1.7962\n",
      "I am working on tract number 620 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 622 2.0 0 90.0 1.5334 199428.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 622 2.0 1 90.0 1.338 283364.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 622 3.0 0 90.0 1.4083 199428.3\n",
      "we have 2 non-opposing shorted wedges for tract no 628\n",
      "we have 2 non-opposing shorted wedges for tract no 629\n",
      "we have 2 non-opposing shorted wedges for tract no 631\n",
      "we have 2 non-opposing shorted wedges for tract no 632\n",
      "7 yoyos for tract,wedge,wedgePop,r= 634 1 374096.03028764704 2.083\n",
      "8 yoyos for tract,wedge,wedgePop,r= 634 1 20145.709749182453 1.0415\n",
      "9 yoyos for tract,wedge,wedgePop,r= 634 1 374252.2902463911 2.1157\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 635 4.0 1 36.3 2.3835 371794.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 635 9.0 1 36.3 2.2334 371794.9\n",
      "I am working on tract number 640 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 647\n",
      "we have 2 non-opposing shorted wedges for tract no 648\n",
      "we have 2 non-opposing shorted wedges for tract no 654\n",
      "I am working on tract number 660 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 669\n",
      "I am working on tract number 680 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 682 3 201083.29271981472 2.5186\n",
      "8 yoyos for tract,wedge,wedgePop,r= 682 3 63274.83167647023 1.2593\n",
      "9 yoyos for tract,wedge,wedgePop,r= 682 3 201081.87394738037 2.5185\n",
      "10 yoyos for tract,wedge,wedgePop,r= 682 3 118121.50738774086 1.8889\n",
      "10 yoyos for tract,wedge,wedgePop,r= 682 3 141841.35291805124 2.2037\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 690 4.0 1 38.0 2.9477 461733.3\n",
      "we have 2 non-opposing shorted wedges for tract no 691\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 692 4.0 1 36.2 3.0403 437705.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 693 13.0 1 36.6 2.6919 430914.2\n",
      "I am working on tract number 700 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 713\n",
      "we have 2 non-opposing shorted wedges for tract no 714\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 715 3.0 0 85.9 1.5517 207110.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 715 4.0 0 85.9 1.3839 207110.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 715 5.0 0 85.9 1.2343 207110.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 715 6.0 0 85.9 1.1008 207110.7\n",
      "I am working on tract number 720 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 736 17.0 2 90.0 3.8952 207259.2\n",
      "loop31.0, tr736,wedgePops690050.96, 154717.7, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,1.2736, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop32.0, tr736,wedgePops693975.59, 158642.3, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.2884, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop33.0, tr736,wedgePops733698.72, 198365.4, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.9456, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop34.0, tr736,wedgePops733698.72, 198365.4, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,-0.3587, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 736 34.0 0 86.4 4.0141 198365.4\n",
      "loop35.0, tr736,wedgePops733689.39, 198356.1, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,-0.3122, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop36.0, tr736,wedgePops626644.62, 91311.3, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,-1.851, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop37.0, tr736,wedgePops689465.06, 154131.8, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,1.2651, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop38.0, tr736,wedgePops693807.71, 158474.4, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.2934, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "loop39.0, tr736,wedgePops733698.72, 198365.4, 178226.7, 178528.5, 178578.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6011 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.8838, 178327.5,0.5202, 178327.5,-0.0121, 178327.5,-0.0057\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 4.29327 158474.4236 198365.4262 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.41673 113464.0931 178226.7168 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.32276 179344.8578 178528.5431 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.81603 179896.2276 178578.0297 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 yoyos for tract,wedge,wedgePop,r= 736 0 198365.42621942377 4.2933\n",
      "loop40.0, tr736,wedgePops637291.91, 101958.6, 178226.7, 178528.5, 178578.0, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 203783.8,-2.1466, 203783.8,0.5202, 203783.8,-0.0121, 203783.8,-0.0057\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.14663 198365.4262 101958.6167 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.41673 113464.0931 178226.7168 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.32276 179344.8578 178528.5431 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.81603 179896.2276 178578.0297 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 736, giving up w pop 637291.9062772687\n",
      "I am working on tract number 740 of 1505 tracts\n",
      "I am working on tract number 760 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 774 3.0 1 90.0 2.1495 223766.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 774 12.0 3 -272.4 3.6947 203539.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 774 13.0 3 -272.4 3.3401 203539.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 774 3 203539.93021407307 3.3157\n",
      "I am working on tract number 780 of 1505 tracts\n",
      "I am working on tract number 800 of 1505 tracts\n",
      "I am working on tract number 820 of 1505 tracts\n",
      "I am working on tract number 840 of 1505 tracts\n",
      "I am working on tract number 860 of 1505 tracts\n",
      "I am working on tract number 880 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 888 0 189618.71664791266 2.0524\n",
      "8 yoyos for tract,wedge,wedgePop,r= 888 0 56835.764122920315 1.0262\n",
      "9 yoyos for tract,wedge,wedgePop,r= 888 0 192114.69498824983 2.0547\n",
      "10 yoyos for tract,wedge,wedgePop,r= 888 0 91452.76490233738 1.5405\n",
      "10 yoyos for tract,wedge,wedgePop,r= 888 0 135419.34014104522 1.7976\n",
      "10 yoyos for tract,wedge,wedgePop,r= 888 0 144305.85124010377 1.9262\n",
      "10 yoyos for tract,wedge,wedgePop,r= 888 0 148824.92104101813 1.9904\n",
      "10 yoyos for tract,wedge,wedgePop,r= 888 0 156636.97105615505 2.0226\n",
      "10 yoyos for tract,wedge,wedgePop,r= 888 0 172602.47091583916 2.0387\n",
      "11 yoyos for tract,wedge,wedgePop,r= 888 0 182666.21954723663 2.0467\n",
      "I am working on tract number 900 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 902\n",
      "we have 2 non-opposing shorted wedges for tract no 903\n",
      "we have 2 non-opposing shorted wedges for tract no 904\n",
      "I am working on tract number 920 of 1505 tracts\n",
      "I am working on tract number 940 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 954 6.0 0 110.7 1.8653 184794.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 954 7.0 0 110.7 1.8159 184794.8\n",
      "I am working on tract number 960 of 1505 tracts\n",
      "I am working on tract number 980 of 1505 tracts\n",
      "I am working on tract number 1000 of 1505 tracts\n",
      "I am working on tract number 1020 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1022 10.0 0 104.6 2.4055 221976.4\n",
      "I am working on tract number 1040 of 1505 tracts\n",
      "I am working on tract number 1060 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1064\n",
      "we have 2 non-opposing shorted wedges for tract no 1065\n",
      "we have 2 non-opposing shorted wedges for tract no 1066\n",
      "I am working on tract number 1080 of 1505 tracts\n",
      "I am working on tract number 1100 of 1505 tracts\n",
      "I am working on tract number 1120 of 1505 tracts\n",
      "I am working on tract number 1140 of 1505 tracts\n",
      "I am working on tract number 1160 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1172 11.0 1 38.5 3.8545 220271.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1172 12.0 1 38.5 3.6039 220271.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1172 13.0 1 38.5 3.3696 220271.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1175 2 214429.3990981398 1.4344\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1175 2 72527.14194852448 0.7172\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1175 2 214743.08012903502 1.4474\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1175 2 131863.15001337286 1.0823\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1175 2 149567.67685316375 1.2649\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1175 2 164803.59493279335 1.3561\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1175 2 209568.9672664073 1.4018\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1175 2 186623.16172995366 1.379\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1175 2 174083.01282588925 1.3675\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 6.0 1 48.6 3.3763 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 7.0 1 48.6 3.2723 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 8.0 1 48.6 3.1715 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 9.0 1 48.6 3.0738 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 10.0 1 48.6 2.9791 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 11.0 1 48.6 2.8873 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 12.0 1 48.6 2.7984 185884.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1177 13.0 1 48.6 2.7122 185884.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1177 1 184706.42646329888 2.3758\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1177 1 38547.460418081755 1.1879\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1177 1 184448.35935202104 2.2913\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1177 1 146296.55318513577 1.7396\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1177 1 151782.07208671476 2.0155\n",
      "loop31.0, tr1177,wedgePops704679.23, 178196.5, 169448.3, 178430.2, 178604.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?21013 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.002, 178327.5,0.1379, 178327.5,-0.0003, 178327.5,-0.001\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1177 1 169448.30555621345 2.1534\n",
      "loop32.0, tr1177,wedgePops719477.4, 178196.5, 184246.5, 178430.2, 178604.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?21013 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.002, 178327.5,0.069, 178327.5,-0.0003, 178327.5,-0.001\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1177 1 184246.47790816895 2.2223\n",
      "loop33.0, tr1177,wedgePops719378.31, 178196.5, 184147.4, 178430.2, 178604.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?21113 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.002, 178327.5,-0.0345, 178327.5,-0.0003, 178327.5,-0.001\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1177 1 184147.39309352246 2.1879\n",
      "loop34.0, tr1177,wedgePops714774.82, 178196.5, 179543.9, 178430.2, 178604.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?21113 \n",
      "   targetWP, latest drx4 are tWP,dr, 178327.5,0.002, 178327.5,-0.0172, 178327.5,-0.0003, 178327.5,-0.001\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1179 3.0 0 90.0 0.9921 258201.5\n",
      "I am working on tract number 1180 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1183 7.0 2 90.0 1.461 210139.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1184 9.0 0 112.0 1.8854 198710.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1184 14.0 0 112.0 1.8383 198710.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1184 19.0 0 112.0 1.8743 198710.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1184 0 198709.98269778697 1.9484\n",
      "I am working on tract number 1200 of 1505 tracts\n",
      "I am working on tract number 1220 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1235 0 206298.87176676388 3.6175\n",
      "I am working on tract number 1240 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 3.0 1 43.1 1.5431 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 4.0 1 43.1 1.3442 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 5.0 1 43.1 1.3146 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 6.0 1 43.1 1.2856 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 7.0 1 43.1 1.2573 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 8.0 1 43.1 1.2296 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 9.0 1 43.1 1.2025 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 10.0 1 43.1 1.176 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 11.0 1 43.1 1.1501 272811.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1240 12.0 1 43.1 1.1248 272811.8\n",
      "we have 2 non-opposing shorted wedges for tract no 1241\n",
      "we have 2 non-opposing shorted wedges for tract no 1242\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1250 3 139817.82392403844 1.8001\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1250 3 244775.192767184 3.9663\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1250 21.0 3 61.2 2.8832 244775.2\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1250 3 244775.192767184 2.8832\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1250 3 244342.49450582586 2.3417\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1250 3 227810.29439399857 2.0709\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1250 3 157956.08006746467 1.9355\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1250 3 174582.14396163123 2.0032\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1250 3 196360.1655576572 2.037\n",
      "I am working on tract number 1260 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1276\n",
      "I am working on tract number 1280 of 1505 tracts\n",
      "I am working on tract number 1300 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1302 12.0 0 110.6 2.7602 216600.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1302 13.0 0 110.6 2.3767 216600.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1305 11.0 0 105.1 0.992 184020.8\n",
      "I am working on tract number 1320 of 1505 tracts\n",
      "I am working on tract number 1340 of 1505 tracts\n",
      "I am working on tract number 1360 of 1505 tracts\n",
      "I am working on tract number 1380 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1380 12.0 0 127.5 2.8609 231303.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1382 5.0 2 90.0 2.1857 197567.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1382 6.0 2 90.0 2.022 197567.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1382 2 197567.81986307885 2.031\n",
      "I am working on tract number 1400 of 1505 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1407 0 191253.38399950592 1.0026\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1407 0 84486.73687833402 0.5013\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1407 0 190929.24628284504 0.9696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1407 0 137520.36940665185 0.7355\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1407 0 151163.34562473433 0.8526\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1407 0 185674.53314195888 0.9111\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1407 0 157648.43252113243 0.8818\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1407 0 171882.180253016 0.8965\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1408 5.0 1 79.6 3.1571 183488.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1408 6.0 1 79.6 3.0901 183488.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1408 7.0 1 79.6 3.0244 183488.1\n",
      "we have 2 non-opposing shorted wedges for tract no 1413\n",
      "we have 2 non-opposing shorted wedges for tract no 1417\n",
      "I am working on tract number 1420 of 1505 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 4.0 1 51.4 1.4026 319043.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1434 3.0 1 46.0 1.4241 326892.7\n",
      "I am working on tract number 1440 of 1505 tracts\n",
      "I am working on tract number 1460 of 1505 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1474\n",
      "I am working on tract number 1480 of 1505 tracts\n",
      "I am working on tract number 1500 of 1505 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0001435980852298\n",
      "Average and max number of wedgePop loops per tract were:  6.346843853820598 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.5376799214761454 12.37714006407971\n",
      "calculated statewide vote was 0.46562010717075347, should have been 0.46360486351433333\n",
      "calcd statewide Hispanic pop was 0.047560278806215876, should have been 0.04984355323826456\n",
      "calcd statewide Black pop was 0.05637269246711512, should have been 0.05933653267131963\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.29169435215946843\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0001435980852298\n",
      "Average and max number of wedgePop loops per tract were:  6.346843853820598 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.5376799214761454 12.37714006407971 for  MN\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start MN 8:21  some interruptions, done 9:42\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MN 18Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"18Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "736 0.89 ( -95.38903468718591 46.58572059033382 ) 0.6438195106445865 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [736]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.7953401881128005 109 pop,GOP 0.6422018348623854\n",
      "7 0.7882920344989939 117 pop,GOP 0.7350427350427351\n",
      "16 0.6438124705297691 81 pop,GOP 0.6790123456790124\n",
      "22 0.6886186465513219 86 pop,GOP 0.6395348837209303\n",
      "25 0.7692217879528493 78 pop,GOP 0.6794871794871795\n",
      "32 0.7941745228493193 248 pop,GOP 0.625\n",
      "34 0.7712061302422035 13 pop,GOP 0.6153846153846154\n",
      "35 0.7618230637346437 224 pop,GOP 0.7366071428571429\n",
      "41 0.7747764564409423 84 pop,GOP 0.7619047619047619\n",
      "42 0.7921984718943552 93 pop,GOP 0.5698924731182796\n",
      "49 0.7166357403046796 92 pop,GOP 0.8804347826086957\n",
      "55 0.624103015501147 10 pop,GOP 0.6\n",
      "60 0.6573697192761244 100 pop,GOP 0.82\n",
      "65 0.6862394287760344 363 pop,GOP 0.7272727272727273\n",
      "66 0.6709587267872731 9 pop,GOP 1.0\n",
      "74 0.6653989694288276 43 pop,GOP 0.7441860465116279\n",
      "80 0.7478579013183118 132 pop,GOP 0.7348484848484849\n",
      "88 0.7821468600507308 64 pop,GOP 0.59375\n",
      "94 0.770934449596568 30 pop,GOP 0.8\n",
      "95 0.6419000922970864 23 pop,GOP 0.782608695652174\n",
      "107 0.6228165686931398 7883 pop,GOP 0.49562349359380947\n",
      "110 0.7167254246954586 62 pop,GOP 0.7580645161290323\n",
      "111 0.7195943762571265 65 pop,GOP 0.7384615384615385\n",
      "117 0.6652281852861686 62 pop,GOP 0.9032258064516129\n",
      "131 0.7377618535836713 74 pop,GOP 0.7972972972972973\n",
      "133 0.6421071765478127 7912 pop,GOP 0.49785136501516686\n",
      "135 0.7156096488352104 94 pop,GOP 0.6808510638297872\n",
      "143 0.7544762450390791 24 pop,GOP 0.5833333333333334\n",
      "144 0.7574994505866783 45 pop,GOP 0.8888888888888888\n",
      "145 0.7322196782709512 30 pop,GOP 0.8\n",
      "154 0.6603381882453893 43 pop,GOP 0.6744186046511628\n",
      "155 0.7539097794757915 170 pop,GOP 0.7\n",
      "164 0.7146782275054228 322 pop,GOP 0.6739130434782609\n",
      "167 0.6759409336697717 289 pop,GOP 0.7612456747404844\n",
      "177 0.7725576060679072 21 pop,GOP 0.47619047619047616\n",
      "194 0.754765270337377 58 pop,GOP 0.6551724137931034\n",
      "222 0.7415669669543048 104 pop,GOP 0.7692307692307693\n",
      "225 0.7208675482055269 44 pop,GOP 0.6136363636363636\n",
      "231 0.7854907828907908 75 pop,GOP 0.5466666666666666\n",
      "247 0.7907336769563202 406 pop,GOP 0.05172413793103448\n",
      "248 0.6246945815733604 28 pop,GOP 0.5714285714285714\n",
      "252 0.746532779134658 39 pop,GOP 0.7948717948717948\n",
      "255 0.7715164692403923 139 pop,GOP 0.7194244604316546\n",
      "261 0.6646546216536476 344 pop,GOP 0.7732558139534884\n",
      "264 0.6180900421129374 23 pop,GOP 0.30434782608695654\n",
      "270 0.730544259474068 4598 pop,GOP 0.4164854284471509\n",
      "272 0.7799732674751221 44 pop,GOP 0.5227272727272727\n",
      "273 0.7749002083897328 173 pop,GOP 0.861271676300578\n",
      "275 0.6854064391013822 47 pop,GOP 0.8936170212765957\n",
      "277 0.666808105671595 24 pop,GOP 0.7916666666666666\n",
      "283 0.6786339258979351 127 pop,GOP 0.7480314960629921\n",
      "284 0.7493634370533762 92 pop,GOP 0.7065217391304348\n",
      "285 0.6836130574817941 103 pop,GOP 0.7184466019417476\n",
      "286 0.7562749339156812 37 pop,GOP 0.6216216216216216\n",
      "288 0.7903993500797163 44 pop,GOP 0.5454545454545454\n",
      "289 0.7035470910195326 24 pop,GOP 0.5\n",
      "290 0.7107284785250847 95 pop,GOP 0.8210526315789474\n",
      "298 0.7447524342582064 55 pop,GOP 0.6363636363636364\n",
      "302 0.6397874855545409 18 pop,GOP 0.8333333333333334\n",
      "305 0.7020477203623783 32 pop,GOP 0.6875\n",
      "316 0.7846721351055186 111 pop,GOP 0.7117117117117117\n",
      "323 0.7668010583226186 139 pop,GOP 0.7122302158273381\n",
      "325 0.7650187184166531 17 pop,GOP 0.7647058823529411\n",
      "326 0.6537769871828509 120 pop,GOP 0.7916666666666666\n",
      "328 0.6854150733012129 23 pop,GOP 0.8695652173913043\n",
      "329 0.7253862510395315 27 pop,GOP 0.8148148148148148\n",
      "337 0.6904389700505081 83 pop,GOP 0.6626506024096386\n",
      "338 0.6639222465264716 52 pop,GOP 0.8461538461538461\n",
      "342 0.7605640199723506 141 pop,GOP 0.6099290780141844\n",
      "349 0.7023112962033345 332 pop,GOP 0.6837349397590361\n",
      "350 0.7998393409756993 131 pop,GOP 0.8625954198473282\n",
      "351 0.6904794731993125 80 pop,GOP 0.75\n",
      "356 0.6740903185403353 29 pop,GOP 0.8620689655172413\n",
      "357 0.7916513242259595 462 pop,GOP 0.7380952380952381\n",
      "366 0.7512958733117648 359 pop,GOP 0.6768802228412256\n",
      "381 0.7107002503847131 31 pop,GOP 0.967741935483871\n",
      "386 0.7731081083436414 77 pop,GOP 0.6233766233766234\n",
      "389 0.613872301071819 10 pop,GOP 0.8\n",
      "406 0.6489659459718724 44 pop,GOP 0.75\n",
      "410 0.7122148062634494 4881 pop,GOP 0.4187666461790617\n",
      "412 0.7560587097630372 23 pop,GOP 0.8260869565217391\n",
      "413 0.6508803660728538 61 pop,GOP 0.639344262295082\n",
      "418 0.7169385569645407 36 pop,GOP 0.75\n",
      "420 0.7524079727207169 124 pop,GOP 0.7741935483870968\n",
      "427 0.7936878156141078 54 pop,GOP 0.6481481481481481\n",
      "433 0.7337818175509568 37 pop,GOP 0.7567567567567568\n",
      "434 0.680158577589236 149 pop,GOP 0.610738255033557\n",
      "436 0.7761586886251869 203 pop,GOP 0.625615763546798\n",
      "437 0.7653626278338491 21 pop,GOP 0.6190476190476191\n",
      "442 0.7168174080556293 60 pop,GOP 0.6166666666666667\n",
      "448 0.7599388204057529 88 pop,GOP 0.8636363636363636\n",
      "455 0.7847010651180564 71 pop,GOP 0.8028169014084507\n",
      "459 0.6232164125651085 60 pop,GOP 0.7166666666666667\n",
      "470 0.6358672292771159 8 pop,GOP 0.75\n",
      "480 0.7947680601141927 873 pop,GOP 0.6391752577319587\n",
      "488 0.7838999837198798 113 pop,GOP 0.8230088495575221\n",
      "493 0.700132329926413 58 pop,GOP 0.5172413793103449\n",
      "495 0.7870854901342045 90 pop,GOP 0.6444444444444445\n",
      "501 0.6913155852077795 123 pop,GOP 0.6422764227642277\n",
      "508 0.7231149042249915 117 pop,GOP 0.5470085470085471\n",
      "511 0.7000016062545549 63 pop,GOP 0.9047619047619048\n",
      "512 0.7185601111138887 15 pop,GOP 0.4\n",
      "528 0.6533102536845075 59 pop,GOP 0.847457627118644\n",
      "535 0.6659381171463633 213 pop,GOP 0.6525821596244131\n",
      "538 0.7012898709684298 87 pop,GOP 0.8620689655172413\n",
      "539 0.7565650171207119 84 pop,GOP 0.7857142857142857\n",
      "541 0.7728390784024965 36 pop,GOP 0.6388888888888888\n",
      "545 0.67204454920213 30 pop,GOP 0.7\n",
      "553 0.7954435523668458 107 pop,GOP 0.897196261682243\n",
      "555 0.7829073617939601 57 pop,GOP 0.6842105263157895\n",
      "556 0.6288245063071246 26 pop,GOP 0.5384615384615384\n",
      "557 0.7487846375165641 57 pop,GOP 0.5614035087719298\n",
      "558 0.7736615069066181 102 pop,GOP 0.6078431372549019\n",
      "560 0.6799493595503404 159 pop,GOP 0.6415094339622641\n",
      "574 0.7505173299621556 86 pop,GOP 0.7441860465116279\n",
      "576 0.6818948056979026 72 pop,GOP 0.7638888888888888\n",
      "588 0.7936005354908372 118 pop,GOP 0.559322033898305\n",
      "596 0.7468231604611034 31 pop,GOP 0.5483870967741935\n",
      "598 0.7428888222013941 98 pop,GOP 0.7040816326530612\n",
      "625 0.7985984884583094 47 pop,GOP 0.851063829787234\n",
      "627 0.0007249059066093085 200 pop,GOP 0.71\n",
      "630 0.7046459569717398 38 pop,GOP 0.7894736842105263\n",
      "635 0.724639176725594 108 pop,GOP 0.5740740740740741\n",
      "640 0.6891556681978742 215 pop,GOP 0.6651162790697674\n",
      "649 0.7941264916929184 578 pop,GOP 0.7058823529411765\n",
      "650 0.7605661692932015 143 pop,GOP 0.6643356643356644\n",
      "652 0.746320543255993 101 pop,GOP 0.6732673267326733\n",
      "662 0.733164820870493 43 pop,GOP 0.627906976744186\n",
      "667 0.7767686007396786 189 pop,GOP 0.6507936507936508\n",
      "670 0.6485684272533074 11230 pop,GOP 0.5153161175422974\n",
      "675 0.6532581886263479 25 pop,GOP 0.84\n",
      "687 0.7503507715219591 63 pop,GOP 0.746031746031746\n",
      "688 0.6534973510485788 51 pop,GOP 0.9019607843137255\n",
      "689 0.6961167132485648 41 pop,GOP 0.7804878048780488\n",
      "694 0.6652425508394029 27 pop,GOP 0.7407407407407407\n",
      "707 0.7367960366014304 206 pop,GOP 0.6213592233009708\n",
      "709 0.6775563198009155 118 pop,GOP 0.8305084745762712\n",
      "721 0.6256550707075482 62 pop,GOP 0.7580645161290323\n",
      "725 0.7239184481804528 80 pop,GOP 0.6625\n",
      "733 0.7946902819188546 337 pop,GOP 0.5014836795252225\n",
      "748 0.7293725736300266 517 pop,GOP 0.5493230174081238\n",
      "754 0.7955939686823099 125 pop,GOP 0.712\n",
      "766 0.6642174085618686 342 pop,GOP 0.7777777777777778\n",
      "768 0.6832013280377802 11 pop,GOP 0.45454545454545453\n",
      "769 0.671589460244555 225 pop,GOP 0.68\n",
      "772 0.774543154214247 539 pop,GOP 0.5565862708719852\n",
      "779 0.7086506096592493 428 pop,GOP 0.5700934579439252\n",
      "781 0.6381038477052621 131 pop,GOP 0.7022900763358778\n",
      "782 0.7372771075379215 36 pop,GOP 0.6944444444444444\n",
      "783 0.6391478977966768 55 pop,GOP 0.8181818181818182\n",
      "787 0.6756451133207928 27 pop,GOP 0.6296296296296297\n",
      "788 0.7910463213473675 656 pop,GOP 0.6280487804878049\n",
      "795 0.7448336338249119 37 pop,GOP 0.7567567567567568\n",
      "799 0.788550886154183 168 pop,GOP 0.6726190476190477\n",
      "805 0.7335835044239983 2163 pop,GOP 0.47619047619047616\n",
      "815 0.7770079448814463 57 pop,GOP 0.5964912280701754\n",
      "823 0.7361270885491519 408 pop,GOP 0.024509803921568627\n",
      "847 0.7864368946488697 6811 pop,GOP 0.42548818088386436\n",
      "855 0.7405844390162379 146 pop,GOP 0.7808219178082192\n",
      "857 0.6164363351946797 32 pop,GOP 0.3125\n",
      "859 0.6732810046390272 14 pop,GOP 0.7142857142857143\n",
      "869 0.6288073850674863 32 pop,GOP 0.875\n",
      "886 0.6850451653659497 32 pop,GOP 0.9375\n",
      "890 0.7390232897471828 32 pop,GOP 0.65625\n",
      "893 0.6551597493192425 223 pop,GOP 0.8116591928251121\n",
      "894 0.746677130738223 58 pop,GOP 0.7068965517241379\n",
      "903 0.7601856189776408 62 pop,GOP 0.6935483870967742\n",
      "904 0.696298137011165 251 pop,GOP 0.7569721115537849\n",
      "913 0.799391959541235 161 pop,GOP 0.6273291925465838\n",
      "918 0.7794294149696964 80 pop,GOP 0.825\n",
      "926 0.7007845462589668 632 pop,GOP 0.6613924050632911\n",
      "928 0.7867541839309554 178 pop,GOP 0.5786516853932584\n",
      "931 0.6643978129106777 19 pop,GOP 1.0\n",
      "934 0.7069202346897818 24 pop,GOP 0.9166666666666666\n",
      "939 0.010883737715938185 530 pop,GOP 0.5716981132075472\n",
      "946 0.7121414014592533 29 pop,GOP 0.7241379310344828\n",
      "953 0.7396567001094414 79 pop,GOP 0.4050632911392405\n",
      "955 0.6489351565302386 19 pop,GOP 1.0\n",
      "962 0.6163712424584968 67 pop,GOP 0.7164179104477612\n",
      "969 0.7276191800924808 110 pop,GOP 0.7545454545454545\n",
      "970 0.7355226093719587 358 pop,GOP 0.7094972067039106\n",
      "972 0.7946255155261907 38 pop,GOP 0.6578947368421053\n",
      "996 0.7229031971437689 218 pop,GOP 0.8807339449541285\n",
      "998 0.6341427278645388 117 pop,GOP 0.8205128205128205\n",
      "1000 0.7193949901856986 57 pop,GOP 0.8421052631578947\n",
      "1004 0.6852012896359864 38 pop,GOP 0.8421052631578947\n",
      "1022 0.7623111469924788 129 pop,GOP 0.6821705426356589\n",
      "1034 0.7778390661273347 150 pop,GOP 0.78\n",
      "1039 0.7565425303724566 117 pop,GOP 0.7606837606837606\n",
      "1043 0.684827966264644 55 pop,GOP 0.7636363636363637\n",
      "1049 0.7049383485264263 385 pop,GOP 0.5844155844155844\n",
      "1052 0.7569276567667553 68 pop,GOP 0.6176470588235294\n",
      "1053 0.7214820842636381 202 pop,GOP 0.7079207920792079\n",
      "1069 0.7415740785361238 55 pop,GOP 0.7454545454545455\n",
      "1072 0.6901985178348043 47 pop,GOP 0.7446808510638298\n",
      "1079 0.6161008859926114 70 pop,GOP 0.6\n",
      "1081 0.7904438734322029 441 pop,GOP 0.5918367346938775\n",
      "1082 0.7507132897196713 547 pop,GOP 0.6069469835466179\n",
      "1087 0.7610768911010071 101 pop,GOP 0.7920792079207921\n",
      "1093 0.6642544670633703 51 pop,GOP 0.8627450980392157\n",
      "1096 0.7128438397726655 31 pop,GOP 0.6774193548387096\n",
      "1106 0.6887760931395362 55 pop,GOP 0.7636363636363637\n",
      "1119 0.7785091134997933 68 pop,GOP 0.8235294117647058\n",
      "1123 0.6161008859926108 23 pop,GOP 0.5217391304347826\n",
      "1127 0.6933083379883588 169 pop,GOP 0.6982248520710059\n",
      "1130 0.6484412272211398 44 pop,GOP 0.6818181818181818\n",
      "1140 0.7028317309151275 16 pop,GOP 0.875\n",
      "1141 0.6797211801809211 191 pop,GOP 0.6963350785340314\n",
      "1146 0.7285372543954856 233 pop,GOP 0.648068669527897\n",
      "1153 0.6638713837713445 75 pop,GOP 0.8533333333333334\n",
      "1155 0.7008434061973029 67 pop,GOP 0.7313432835820896\n",
      "1159 0.7232489149761273 50 pop,GOP 0.54\n",
      "1163 0.6422819527050948 79 pop,GOP 0.7088607594936709\n",
      "1166 0.6183623237465098 20 pop,GOP 0.85\n",
      "1174 0.6996484696834562 64 pop,GOP 0.546875\n",
      "1176 0.6397317699888541 41 pop,GOP 0.8292682926829268\n",
      "1180 0.661698713456715 83 pop,GOP 0.6024096385542169\n",
      "1184 0.7306648669521947 23 pop,GOP 0.43478260869565216\n",
      "1191 0.7267428011324868 279 pop,GOP 0.7347670250896058\n",
      "1197 0.7829861816963429 87 pop,GOP 0.8735632183908046\n",
      "1198 0.6225042900298082 58 pop,GOP 0.7413793103448276\n",
      "1201 0.6271228648207801 74 pop,GOP 0.7972972972972973\n",
      "1202 0.7013110628241421 19 pop,GOP 0.6842105263157895\n",
      "1203 0.6985069418418084 4504 pop,GOP 0.413854351687389\n",
      "1210 0.7222460379161919 4544 pop,GOP 0.41725352112676056\n",
      "1217 0.7049625198518574 40 pop,GOP 0.75\n",
      "1219 0.7218407440132788 229 pop,GOP 0.7510917030567685\n",
      "1221 0.7289585740339245 75 pop,GOP 0.6666666666666666\n",
      "1222 0.7110836831837479 8142 pop,GOP 0.5023335789732253\n",
      "1223 0.6330078274557979 233 pop,GOP 0.5965665236051502\n",
      "1224 0.7344699916734703 211 pop,GOP 0.7014218009478673\n",
      "1237 0.6121786441149057 21 pop,GOP 0.7619047619047619\n",
      "1241 0.7733721819278655 348 pop,GOP 0.5201149425287356\n",
      "1244 0.6704676430823381 157 pop,GOP 0.6815286624203821\n",
      "1245 0.7761173952220145 301 pop,GOP 0.7740863787375415\n",
      "1257 0.7879423952726798 110 pop,GOP 0.6\n",
      "1260 0.6778791883469826 71 pop,GOP 0.6901408450704225\n",
      "1262 0.7305871273092149 53 pop,GOP 0.6981132075471698\n",
      "1269 0.7050987522242054 53 pop,GOP 0.7547169811320755\n",
      "1275 0.6910659260878911 70 pop,GOP 0.7428571428571429\n",
      "1276 0.6779407040164831 73 pop,GOP 0.7534246575342466\n",
      "1278 0.6330299824216505 18 pop,GOP 0.5555555555555556\n",
      "1279 0.7717625402368593 69 pop,GOP 0.8695652173913043\n",
      "1280 0.7864709679832446 104 pop,GOP 0.75\n",
      "1290 0.5910582863965855 7912 pop,GOP 0.49519716885743176\n",
      "1291 0.6847236942104138 21 pop,GOP 0.5714285714285714\n",
      "1296 0.7505622352564216 77 pop,GOP 0.6233766233766234\n",
      "1311 0.7947083363456734 68 pop,GOP 0.6470588235294118\n",
      "1312 0.6782937078852663 27 pop,GOP 0.6296296296296297\n",
      "1322 0.6214620266371457 11 pop,GOP 0.9090909090909091\n",
      "1323 0.7491179918600239 238 pop,GOP 0.5252100840336135\n",
      "1324 0.7243751590306425 375 pop,GOP 0.8133333333333334\n",
      "1326 0.6817048895808122 49 pop,GOP 0.7959183673469388\n",
      "1327 0.7711481269347343 46 pop,GOP 0.45652173913043476\n",
      "1330 0.754834510964705 181 pop,GOP 0.5193370165745856\n",
      "1340 0.7387511423042558 48 pop,GOP 0.5625\n",
      "1346 0.6246667377963877 73 pop,GOP 0.684931506849315\n",
      "1353 0.7975499635082453 286 pop,GOP 0.6783216783216783\n",
      "1357 0.7396039027794178 97 pop,GOP 0.6907216494845361\n",
      "1360 0.6624418918544417 78 pop,GOP 0.8076923076923077\n",
      "1361 0.7443579660686583 73 pop,GOP 0.589041095890411\n",
      "1363 0.6240212027536958 27 pop,GOP 0.25925925925925924\n",
      "1365 0.7752761607394101 63 pop,GOP 0.8571428571428571\n",
      "1366 0.6344831184915511 17 pop,GOP 0.7647058823529411\n",
      "1377 0.6631825349103326 72 pop,GOP 0.875\n",
      "1380 0.7811466006867323 90 pop,GOP 0.6333333333333333\n",
      "1387 0.6556311452556814 103 pop,GOP 0.7087378640776699\n",
      "1396 0.7317854814768335 69 pop,GOP 0.782608695652174\n",
      "1400 0.6760569543885546 54 pop,GOP 0.7962962962962963\n",
      "1401 0.7459877925996546 148 pop,GOP 0.831081081081081\n",
      "1410 0.6654362164147525 73 pop,GOP 0.821917808219178\n",
      "1419 0.7840481477305696 108 pop,GOP 0.5370370370370371\n",
      "1420 0.6897452559165655 488 pop,GOP 0.8237704918032787\n",
      "1427 0.6810380820694706 87 pop,GOP 0.8620689655172413\n",
      "1428 0.7072616592505966 126 pop,GOP 0.8571428571428571\n",
      "1431 0.7701380756352726 50 pop,GOP 0.48\n",
      "1436 0.7550154454976374 128 pop,GOP 0.6875\n",
      "1437 0.6523267189755281 220 pop,GOP 0.5681818181818182\n",
      "1439 0.7785696028642586 127 pop,GOP 0.7401574803149606\n",
      "1443 0.6694356162081218 81 pop,GOP 0.5802469135802469\n",
      "1447 0.6756644902086605 104 pop,GOP 0.7596153846153846\n",
      "1457 0.6529905450539679 44 pop,GOP 0.6590909090909091\n",
      "1459 0.6636051403103723 47 pop,GOP 0.851063829787234\n",
      "1461 0.6161008859926107 523 pop,GOP 0.5086042065009561\n",
      "1468 0.5927080081223731 7780 pop,GOP 0.49241645244215937\n",
      "1470 0.7947511072667061 88 pop,GOP 0.6818181818181818\n",
      "1471 0.7216007048582574 64 pop,GOP 0.6875\n",
      "1486 0.7839046254389389 88 pop,GOP 0.7159090909090909\n",
      "1488 0.7334984229250913 258 pop,GOP 0.5891472868217055\n",
      "1490 0.6292209328690576 389 pop,GOP 0.6401028277634961\n",
      "1497 0.7193168609516094 158 pop,GOP 0.7278481012658228\n",
      "1506 0.6641138298273612 70 pop,GOP 0.7857142857142857\n",
      "1509 0.7605821484987084 132 pop,GOP 0.803030303030303\n",
      "1511 0.7437493278011151 74 pop,GOP 0.6351351351351351\n",
      "1518 0.7521285176102734 46 pop,GOP 0.5869565217391305\n",
      "1520 0.7611563683129684 85 pop,GOP 0.7647058823529411\n",
      "1525 0.695835250635914 51 pop,GOP 0.803921568627451\n",
      "1527 0.6793423930804501 27 pop,GOP 0.6296296296296297\n",
      "1535 0.778093170153968 31 pop,GOP 0.6129032258064516\n",
      "1549 0.6299795794567272 31 pop,GOP 0.8709677419354839\n",
      "1550 0.7409959907313393 69 pop,GOP 0.7246376811594203\n",
      "1553 0.6962890545434446 47 pop,GOP 0.8085106382978723\n",
      "1554 0.7786145076333363 51 pop,GOP 0.6470588235294118\n",
      "1559 0.7822277361047977 215 pop,GOP 0.7534883720930232\n",
      "1563 0.7232666525056396 66 pop,GOP 0.803030303030303\n",
      "1567 0.32629979704298107 612 pop,GOP 0.44607843137254904\n",
      "1577 0.6829121530573599 97 pop,GOP 0.7525773195876289\n",
      "1583 0.7201347261439854 73 pop,GOP 0.863013698630137\n",
      "1588 0.725542494661825 214 pop,GOP 0.7429906542056075\n",
      "1593 0.6183968147809709 20 pop,GOP 0.55\n",
      "1597 0.6979832081972536 235 pop,GOP 0.7404255319148936\n",
      "1598 0.7939857888115613 221 pop,GOP 0.6380090497737556\n",
      "1604 0.7725754779725386 35 pop,GOP 0.7714285714285715\n",
      "1608 0.7390755576581243 34 pop,GOP 0.7647058823529411\n",
      "1612 0.7502770320698317 32 pop,GOP 0.875\n",
      "1620 0.7560935685500128 48 pop,GOP 0.4791666666666667\n",
      "1626 0.6286029606161787 13 pop,GOP 1.0\n",
      "1632 0.7296922332491647 54 pop,GOP 0.5\n",
      "1633 0.7631786027008322 63 pop,GOP 0.7619047619047619\n",
      "1637 0.7418819933863212 82 pop,GOP 0.6097560975609756\n",
      "1646 0.7962533152530236 105 pop,GOP 0.6\n",
      "1647 0.7609350235335522 61 pop,GOP 0.5081967213114754\n",
      "1648 0.7370010847241483 34 pop,GOP 0.8235294117647058\n",
      "1656 0.6857984819881121 163 pop,GOP 0.7423312883435583\n",
      "1664 0.6562344899149644 39 pop,GOP 0.7435897435897436\n",
      "1672 0.7750749662455556 829 pop,GOP 0.5886610373944512\n",
      "1677 0.6129621336250699 36 pop,GOP 0.6111111111111112\n",
      "1678 0.7987732749272517 99 pop,GOP 0.6262626262626263\n",
      "1680 0.678439542956561 932 pop,GOP 0.7746781115879828\n",
      "1698 0.6718729706798335 79 pop,GOP 0.8481012658227848\n",
      "1703 0.685823498405209 119 pop,GOP 0.6638655462184874\n",
      "1704 0.6196631514237935 117 pop,GOP 0.358974358974359\n",
      "1705 0.7679651785522533 29 pop,GOP 0.7241379310344828\n",
      "1707 0.743424829994349 276 pop,GOP 0.5108695652173914\n",
      "1716 0.7994711382010153 351 pop,GOP 0.6353276353276354\n",
      "1722 0.7538173609306174 98 pop,GOP 0.7653061224489796\n",
      "1725 0.7936513638045589 106 pop,GOP 0.6886792452830188\n",
      "1727 0.6986807594314517 40 pop,GOP 0.75\n",
      "1728 0.7402318284243494 56 pop,GOP 0.6607142857142857\n",
      "1732 0.7837102583541335 84 pop,GOP 0.6547619047619048\n",
      "1734 0.7441620602341336 146 pop,GOP 0.815068493150685\n",
      "1736 0.7533683425320382 43 pop,GOP 0.627906976744186\n",
      "1737 0.7705861285999366 65 pop,GOP 0.6\n",
      "1738 0.7922280181747517 96 pop,GOP 0.6458333333333334\n",
      "1746 0.7733749733344583 35 pop,GOP 0.7714285714285715\n",
      "1766 0.0005490522860855564 440 pop,GOP 0.725\n",
      "1767 0.7079067594100849 89 pop,GOP 0.7191011235955056\n",
      "1771 0.6178492538147348 21 pop,GOP 1.0\n",
      "1772 0.7653759909278313 42 pop,GOP 0.7380952380952381\n",
      "1774 0.7537424122466166 169 pop,GOP 0.6745562130177515\n",
      "1782 0.6279787649960469 85 pop,GOP 0.6941176470588235\n",
      "1796 0.7231107884954082 116 pop,GOP 0.7068965517241379\n",
      "1799 0.7273875902804602 31 pop,GOP 0.7741935483870968\n",
      "1809 0.7451310035923153 52 pop,GOP 0.8076923076923077\n",
      "1811 0.6993670245236351 25 pop,GOP 0.72\n",
      "1815 0.7013508754690123 39 pop,GOP 0.7435897435897436\n",
      "1821 0.6912779026392235 28 pop,GOP 0.75\n",
      "1824 0.7363817469237448 15 pop,GOP 0.6666666666666666\n",
      "1831 0.7073475700576219 139 pop,GOP 0.4748201438848921\n",
      "1832 0.6340064196656149 444 pop,GOP 0.6981981981981982\n",
      "1836 0.7710559405354521 74 pop,GOP 0.8513513513513513\n",
      "1842 0.765167714818509 73 pop,GOP 0.6575342465753424\n",
      "1845 0.7011088553310898 27 pop,GOP 0.7777777777777778\n",
      "1847 0.7275907076651423 37 pop,GOP 0.5945945945945946\n",
      "1848 0.6159173582270039 14 pop,GOP 0.42857142857142855\n",
      "1851 0.684256932565184 176 pop,GOP 0.6988636363636364\n",
      "1853 0.718339837759053 103 pop,GOP 0.9514563106796117\n",
      "1865 0.7602060346708535 683 pop,GOP 0.6222547584187409\n",
      "1869 0.7986219015947842 55 pop,GOP 0.6181818181818182\n",
      "1872 6.251720464359221e-07 461 pop,GOP 0.5097613882863341\n",
      "1876 0.7343607035545936 105 pop,GOP 0.6476190476190476\n",
      "1883 0.6214410482183187 201 pop,GOP 0.6616915422885572\n",
      "1894 0.6056087166630646 188 pop,GOP 0.48404255319148937\n",
      "1897 0.7814300096038039 1293 pop,GOP 0.44779582366589327\n",
      "1899 0.752097481398673 6 pop,GOP 0.6666666666666666\n",
      "1908 0.6992779367824232 59 pop,GOP 0.7627118644067796\n",
      "1909 0.06674291237920506 116 pop,GOP 0.7586206896551724\n",
      "1910 0.5285893487394354 334 pop,GOP 0.7574850299401198\n",
      "1915 0.6787955426508319 101 pop,GOP 0.8217821782178217\n",
      "1917 0.7014530373790129 27 pop,GOP 0.7037037037037037\n",
      "1922 0.7582910236506607 19 pop,GOP 0.8947368421052632\n",
      "1924 0.6850533358236953 64 pop,GOP 0.875\n",
      "1929 0.6854983358044692 56 pop,GOP 0.7321428571428571\n",
      "1931 0.48237412195368246 5001 pop,GOP 0.41451709658068386\n",
      "1945 0.7843686809370269 121 pop,GOP 0.6115702479338843\n",
      "1948 0.7620305337905339 30 pop,GOP 0.5\n",
      "1950 0.7448796017163789 17 pop,GOP 0.6470588235294118\n",
      "1955 0.7906981632033586 23 pop,GOP 0.782608695652174\n",
      "1958 0.775293202641658 275 pop,GOP 0.72\n",
      "1964 0.7854245493296123 86 pop,GOP 0.5581395348837209\n",
      "1974 0.6927524501045227 916 pop,GOP 0.6037117903930131\n",
      "1975 0.6182634845282884 29 pop,GOP 0.7241379310344828\n",
      "1977 0.7194836888092688 138 pop,GOP 0.5942028985507246\n",
      "1986 0.6838998361142989 49 pop,GOP 0.6938775510204082\n",
      "1989 0.7841161654616925 30 pop,GOP 0.6\n",
      "1993 0.620607175464278 98 pop,GOP 0.5408163265306123\n",
      "2013 0.6640479251917474 151 pop,GOP 0.7218543046357616\n",
      "2016 0.794135827753911 182 pop,GOP 0.6043956043956044\n",
      "2023 0.7754198176171294 81 pop,GOP 0.8024691358024691\n",
      "2034 0.7242120606266073 133 pop,GOP 0.6165413533834586\n",
      "2037 0.7170096190249082 50 pop,GOP 0.7\n",
      "2038 0.7437339732756322 45 pop,GOP 0.5777777777777777\n",
      "2042 0.7344774572371383 27 pop,GOP 0.8518518518518519\n",
      "2049 0.6672918420078925 386 pop,GOP 0.7253886010362695\n",
      "2057 0.7823666403991636 76 pop,GOP 0.6973684210526315\n",
      "2078 0.7980387020116032 2234 pop,GOP 0.5094001790510295\n",
      "2086 0.616100885992611 24 pop,GOP 0.75\n",
      "2087 0.7733937397521754 41 pop,GOP 0.6097560975609756\n",
      "2099 0.6665699598682168 114 pop,GOP 0.543859649122807\n",
      "2105 0.6870653259014726 84 pop,GOP 0.6785714285714286\n",
      "2106 0.680592635370027 809 pop,GOP 0.681087762669963\n",
      "2113 0.7382451058496544 40 pop,GOP 0.925\n",
      "2115 0.7210803679866482 29 pop,GOP 0.8275862068965517\n",
      "2141 0.7827808492823326 55 pop,GOP 0.6545454545454545\n",
      "2153 0.6196104471315168 105 pop,GOP 0.5428571428571428\n",
      "2155 0.791609850466057 194 pop,GOP 0.7989690721649485\n",
      "2157 0.7037321740191774 385 pop,GOP 0.6519480519480519\n",
      "2158 0.7395014450363152 102 pop,GOP 0.6764705882352942\n",
      "2163 0.7282598501412202 144 pop,GOP 0.7152777777777778\n",
      "2169 0.7904349795492129 96 pop,GOP 0.6041666666666666\n",
      "2172 0.7877299712338351 158 pop,GOP 0.5126582278481012\n",
      "2180 0.671723176011803 40 pop,GOP 0.8\n",
      "2184 0.7826437281890272 51 pop,GOP 0.8823529411764706\n",
      "2192 0.6346862200702235 5378 pop,GOP 0.4460766084046114\n",
      "2195 0.785015059147368 61 pop,GOP 0.5245901639344263\n",
      "2197 0.6789994122061703 78 pop,GOP 0.6538461538461539\n",
      "2198 0.7998490787116435 306 pop,GOP 0.6830065359477124\n",
      "2199 0.6502841445197579 8148 pop,GOP 0.5025773195876289\n",
      "2202 0.7494103706932403 75 pop,GOP 0.68\n",
      "2204 0.7355275000764562 65 pop,GOP 0.7076923076923077\n",
      "2207 0.7445015774223721 145 pop,GOP 0.6413793103448275\n",
      "2221 0.7578216385669456 49 pop,GOP 0.7551020408163265\n",
      "2223 0.7586306069374283 53 pop,GOP 0.7924528301886793\n",
      "2229 0.7001605040271144 155 pop,GOP 0.832258064516129\n",
      "2246 0.7297358365886151 31 pop,GOP 0.7741935483870968\n",
      "2251 0.6160881906995629 20 pop,GOP 0.7\n",
      "2255 0.6260447120033867 111 pop,GOP 0.7567567567567568\n",
      "2262 0.7351145570495808 45 pop,GOP 0.8666666666666667\n",
      "2270 0.6763647128699262 55 pop,GOP 0.7636363636363637\n",
      "2271 0.6154933302551646 45 pop,GOP 0.7111111111111111\n",
      "2281 0.711990642611498 21 pop,GOP 0.7142857142857143\n",
      "2282 0.7563359511322069 38 pop,GOP 0.6052631578947368\n",
      "2286 0.7161511673840745 66 pop,GOP 0.7575757575757576\n",
      "2287 0.7033571978355668 181 pop,GOP 0.7900552486187845\n",
      "2292 0.7537087367401261 182 pop,GOP 0.7252747252747253\n",
      "2298 0.6764673488984835 279 pop,GOP 0.5698924731182796\n",
      "2306 0.6637697749754706 158 pop,GOP 0.8417721518987342\n",
      "2307 0.7925905015029806 32 pop,GOP 0.625\n",
      "2308 0.6236162209381838 3 pop,GOP 1.0\n",
      "2311 0.7718142252187201 301 pop,GOP 0.7209302325581395\n",
      "2313 0.7599244136219397 46 pop,GOP 0.9565217391304348\n",
      "2314 0.7304440750358485 114 pop,GOP 0.8245614035087719\n",
      "2328 0.7912423923260928 27 pop,GOP 0.7037037037037037\n",
      "2332 0.7081162720537241 40 pop,GOP 0.75\n",
      "2338 0.7663849569209107 111 pop,GOP 0.5135135135135135\n",
      "2345 0.6738421629405603 43 pop,GOP 0.5813953488372093\n",
      "2347 0.7793023181767451 147 pop,GOP 0.7551020408163265\n",
      "2352 0.7808666715230553 51 pop,GOP 0.6862745098039216\n",
      "2355 0.7557056618294701 118 pop,GOP 0.8135593220338984\n",
      "2366 0.6546569451932873 73 pop,GOP 0.7534246575342466\n",
      "2374 0.6990634520815268 186 pop,GOP 0.6505376344086021\n",
      "2377 0.7373763156049962 710 pop,GOP 0.6126760563380281\n",
      "2387 0.7796976319731208 111 pop,GOP 0.6576576576576577\n",
      "2396 0.6859650493874514 38 pop,GOP 0.6842105263157895\n",
      "2398 0.6717154467016608 379 pop,GOP 0.5699208443271768\n",
      "2411 0.706978102125372 55 pop,GOP 0.6909090909090909\n",
      "2415 0.6531281079751734 55 pop,GOP 0.7818181818181819\n",
      "2417 0.7127485832156909 23 pop,GOP 0.4782608695652174\n",
      "2423 0.7175736403506788 44 pop,GOP 0.7954545454545454\n",
      "2425 0.6503873136790189 55 pop,GOP 0.7454545454545455\n",
      "2434 0.7797797941765349 65 pop,GOP 0.6615384615384615\n",
      "2435 0.6684384176401379 84 pop,GOP 0.7976190476190477\n",
      "2440 0.6450997164200802 333 pop,GOP 0.6816816816816816\n",
      "2442 0.6237700601187732 13 pop,GOP 0.8461538461538461\n",
      "2458 0.637054939788372 211 pop,GOP 0.7962085308056872\n",
      "2465 0.7941454776589706 317 pop,GOP 0.6656151419558359\n",
      "2471 0.781289905284886 225 pop,GOP 0.8266666666666667\n",
      "2472 0.7855285033929852 225 pop,GOP 0.6933333333333334\n",
      "2475 0.7260952105408802 40 pop,GOP 0.65\n",
      "2478 0.7579273334974711 61 pop,GOP 0.5901639344262295\n",
      "2480 0.7007055426936086 206 pop,GOP 0.5533980582524272\n",
      "2483 0.6036590193489667 8100 pop,GOP 0.49580246913580245\n",
      "2485 0.7646091646827815 58 pop,GOP 0.7241379310344828\n",
      "2501 0.7004615395547638 156 pop,GOP 0.7884615384615384\n",
      "2508 0.7858760500371419 140 pop,GOP 0.5571428571428572\n",
      "2516 0.6368006802866562 83 pop,GOP 0.7108433734939759\n",
      "2522 0.7617970946661219 88 pop,GOP 0.7045454545454546\n",
      "2525 0.7133356414387466 18 pop,GOP 0.7777777777777778\n",
      "2526 0.7457369704651801 75 pop,GOP 0.6133333333333333\n",
      "2527 0.7524384808949334 143 pop,GOP 0.6503496503496503\n",
      "2529 0.7512257611853953 33 pop,GOP 0.696969696969697\n",
      "2530 0.6775923911849746 248 pop,GOP 0.6491935483870968\n",
      "2532 0.6171593594330311 52 pop,GOP 0.46153846153846156\n",
      "2541 0.6877116481240361 307 pop,GOP 0.6579804560260586\n",
      "2549 0.7507953984794177 56 pop,GOP 0.6785714285714286\n",
      "2554 0.7811328536246488 122 pop,GOP 0.9098360655737705\n",
      "2561 0.6436200983673496 46 pop,GOP 0.6086956521739131\n",
      "2567 0.700832415800066 114 pop,GOP 0.8859649122807017\n",
      "2571 0.6087346641949269 74 pop,GOP 0.6081081081081081\n",
      "2572 0.7394165919482274 37 pop,GOP 0.8378378378378378\n",
      "2582 0.6986866940050368 64 pop,GOP 0.703125\n",
      "2583 0.6214229444099787 6 pop,GOP 1.0\n",
      "2585 0.7000194761404355 62 pop,GOP 0.7258064516129032\n",
      "2586 0.6876958409076477 94 pop,GOP 0.5957446808510638\n",
      "2596 0.7992274255006715 1172 pop,GOP 0.6254266211604096\n",
      "2612 0.7174054847443772 53 pop,GOP 0.7735849056603774\n",
      "2614 0.621307803500716 7908 pop,GOP 0.49418310571573093\n",
      "2621 0.7337801565706731 271 pop,GOP 0.8044280442804428\n",
      "2623 0.7631819572561109 88 pop,GOP 0.8068181818181818\n",
      "2626 0.712204741590659 36 pop,GOP 0.8333333333333334\n",
      "2644 0.6894117673129635 79 pop,GOP 0.7848101265822784\n",
      "2654 0.2517816918123039 486 pop,GOP 0.5596707818930041\n",
      "2658 0.7199415731503921 60 pop,GOP 0.8666666666666667\n",
      "2661 0.6349631540992099 7991 pop,GOP 0.49355524965586284\n",
      "2662 0.7784744811896266 86 pop,GOP 0.8488372093023255\n",
      "2671 0.0002612700081255547 725 pop,GOP 0.646896551724138\n",
      "2673 0.7544850909501148 86 pop,GOP 0.7441860465116279\n",
      "2675 0.7800114887510133 114 pop,GOP 0.8333333333333334\n",
      "2678 0.783134673443812 32 pop,GOP 0.84375\n",
      "2681 0.7904862818259349 158 pop,GOP 0.6835443037974683\n",
      "2687 0.00012175928956312112 172 pop,GOP 0.7151162790697675\n",
      "2691 0.7539924944609737 63 pop,GOP 0.6825396825396826\n",
      "2693 0.6219940591492464 67 pop,GOP 0.6119402985074627\n",
      "2694 0.6701889609024386 1345 pop,GOP 0.6691449814126395\n",
      "2703 0.7388970708657265 71 pop,GOP 0.8450704225352113\n",
      "2706 0.763095589333052 58 pop,GOP 0.7413793103448276\n",
      "2708 0.7081225035519837 732 pop,GOP 0.6024590163934426\n",
      "2711 0.6970368971545994 470 pop,GOP 0.6617021276595745\n",
      "2712 0.6754882834299266 93 pop,GOP 0.8064516129032258\n",
      "2714 0.6584123636096466 11 pop,GOP 0.8181818181818182\n",
      "2715 0.6925839662983139 74 pop,GOP 0.7702702702702703\n",
      "2716 0.6652281852861689 805 pop,GOP 0.7478260869565218\n",
      "2717 0.7216195856091266 332 pop,GOP 0.6897590361445783\n",
      "2718 0.7089376404490901 279 pop,GOP 0.5376344086021505\n",
      "2719 0.7152689088109762 1053 pop,GOP 0.5745489078822412\n",
      "2720 0.7115539799647645 748 pop,GOP 0.6350267379679144\n",
      "2721 0.7044826328033451 425 pop,GOP 0.6141176470588235\n",
      "2722 0.7045793069466609 749 pop,GOP 0.6234979973297731\n",
      "2723 0.712981346393515 413 pop,GOP 0.612590799031477\n",
      "2724 0.7317210405236987 75 pop,GOP 0.5333333333333333\n",
      "2725 0.7405252123646711 32 pop,GOP 0.71875\n",
      "2726 0.742023674485204 242 pop,GOP 0.6652892561983471\n",
      "2727 0.7390221792476248 222 pop,GOP 0.5765765765765766\n",
      "2728 0.6929205746374574 878 pop,GOP 0.5797266514806378\n",
      "2729 0.6935431326636535 489 pop,GOP 0.4785276073619632\n",
      "2730 0.6954237317543333 802 pop,GOP 0.5374064837905237\n",
      "2731 0.6935425212706773 1144 pop,GOP 0.6363636363636364\n",
      "2732 0.7288811680678252 491 pop,GOP 0.6293279022403259\n",
      "2733 0.7296391594048944 638 pop,GOP 0.5705329153605015\n",
      "2734 0.7296391594048947 502 pop,GOP 0.545816733067729\n",
      "2735 0.7316672126844367 514 pop,GOP 0.5622568093385214\n",
      "2736 0.7308209762577256 423 pop,GOP 0.5555555555555556\n",
      "2745 0.6735779564828631 87 pop,GOP 0.8160919540229885\n",
      "2746 0.6671431438895725 131 pop,GOP 0.7099236641221374\n",
      "2747 0.6714405998338765 38 pop,GOP 0.631578947368421\n",
      "2748 0.6692005883441355 285 pop,GOP 0.6175438596491228\n",
      "2749 0.6848351604946504 201 pop,GOP 0.8109452736318408\n",
      "2750 0.6914898749124085 260 pop,GOP 0.6807692307692308\n",
      "2751 0.675139438374763 134 pop,GOP 0.7238805970149254\n",
      "2752 0.6510109684005605 220 pop,GOP 0.7318181818181818\n",
      "2753 0.6882436465032055 73 pop,GOP 0.6986301369863014\n",
      "2754 0.6898890767973611 81 pop,GOP 0.8271604938271605\n",
      "2755 0.6790190074963676 60 pop,GOP 0.6\n",
      "2756 0.6642758652806876 306 pop,GOP 0.6535947712418301\n",
      "2757 0.706608595523507 81 pop,GOP 0.7777777777777778\n",
      "2758 0.6926916054360129 98 pop,GOP 0.7448979591836735\n",
      "2760 0.6857338663242721 70 pop,GOP 0.7285714285714285\n",
      "2761 0.7012171542418673 33 pop,GOP 0.42424242424242425\n",
      "2763 0.6689402027513668 4751 pop,GOP 0.4300147337402652\n",
      "2764 0.652320849266831 4777 pop,GOP 0.4303956458028051\n",
      "2765 0.6400302929649304 5663 pop,GOP 0.4414621225498852\n",
      "2766 0.6406055133002544 5306 pop,GOP 0.4264983038070109\n",
      "2768 0.7754820483774703 396 pop,GOP 0.5454545454545454\n",
      "2796 0.7584459266920246 2214 pop,GOP 0.46747967479674796\n",
      "2797 0.7882200288780673 1473 pop,GOP 0.41683638832315\n",
      "2798 0.7553730333262546 544 pop,GOP 0.34375\n",
      "2799 0.7483179041895729 210 pop,GOP 0.29523809523809524\n",
      "2800 0.745253155815476 1927 pop,GOP 0.3269330565646082\n",
      "2801 0.7248995993851672 1450 pop,GOP 0.3137931034482759\n",
      "2802 0.7287315104818533 189 pop,GOP 0.30687830687830686\n",
      "2803 0.7818021908444245 2440 pop,GOP 0.41270491803278686\n",
      "2804 0.7922198181109887 2002 pop,GOP 0.45404595404595405\n",
      "2805 0.7728708709092359 1921 pop,GOP 0.4367516918271733\n",
      "2806 0.7286790120154598 1174 pop,GOP 0.3798977853492334\n",
      "2807 0.754885529685634 871 pop,GOP 0.399540757749713\n",
      "2808 0.741796606375146 866 pop,GOP 0.42725173210161665\n",
      "2809 0.7804626376946293 1 pop,GOP 0.0\n",
      "2810 0.7040520067463109 606 pop,GOP 0.7013201320132013\n",
      "2811 0.7058596364517343 349 pop,GOP 0.6160458452722063\n",
      "2812 0.7835638638829887 1027 pop,GOP 0.5929892891918208\n",
      "2816 0.7936694960346058 933 pop,GOP 0.6387995712754555\n",
      "2818 0.7936193250437422 996 pop,GOP 0.5863453815261044\n",
      "2854 0.672130820311898 360 pop,GOP 0.5194444444444445\n",
      "2855 0.7369605013721362 693 pop,GOP 0.5844155844155844\n",
      "2860 0.7947073331801393 522 pop,GOP 0.5632183908045977\n",
      "2862 0.7977607593647241 462 pop,GOP 0.3874458874458874\n",
      "2973 0.7608620892209635 161 pop,GOP 0.7267080745341615\n",
      "2974 0.7650714869598805 1161 pop,GOP 0.5839793281653747\n",
      "2975 0.7591480634036981 338 pop,GOP 0.6124260355029586\n",
      "2976 0.760068853851787 314 pop,GOP 0.6592356687898089\n",
      "3043 0.7649972011954758 1858 pop,GOP 0.48277717976318624\n",
      "3044 0.7991078372954883 2400 pop,GOP 0.45666666666666667\n",
      "3045 0.7767605920134311 1719 pop,GOP 0.4490983129726585\n",
      "3046 0.7978783172606069 1730 pop,GOP 0.41965317919075146\n",
      "3047 0.793440917033787 1842 pop,GOP 0.38870792616720956\n",
      "4029 0.6862840198606828 66 pop,GOP 0.5606060606060606\n",
      "4055 0.7258191909080741 7719 pop,GOP 0.4913848944163752\n",
      "4065 0.7365705924070235 824 pop,GOP 0.5691747572815534\n",
      "4095 0.6932656926724566 13 pop,GOP 0.8461538461538461\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13855363008665414 = MN std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1465804654195615 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for MN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.0235\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00202 0.46562 HD avg vs true 0.4636\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.04756, should have been 0.04984\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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trqrdwJ8Cv7xB4wRj7BfwUeDHk2xO8mLgR4CzKzxnB+Ps1RdYeKZJkpcD3wecW9Ep147jwM3Du/muA56pqouT3qnPoFZYVV1KcjvwIAvvJLqnqs4kuW24fjfwm8B3An8wPDO4VBvwtyuPuVcajLNfVXU2yV8Bp4GvAx+sqpFvHV7Pxvy39dvAHyV5nIWXsN5VVRvyf8OR5E+ANwBbk5wHfgt4EfzfXp1g4Z18c8CzLDz7nPxxh7cISpLUii/xSZJaMlCSpJYMlCSpJQMlSWrJQEmSWjJQkqSWDJQkqaX/BacyS9eVboe7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for GA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  765136.2142857143\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.533 2.514\n",
      "fractional expected GOP seats =  3.767  out of  8 seats for MN\n",
      "simpler expected GOP seats =  3.7945  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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